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-Named Entity Recognition and Classification on Historical
-Documents: A Survey
-MAUD EHRMANN, Ecole Polytechnique Fédérale de Lausanne
-AHMED HAMDI, University of La Rochelle
-ELVYS LINHARES PONTES, University of La Rochelle
-MATTEO ROMANELLO, Ecole Polytechnique Fédérale de Lausanne
-ANTOINE DOUCET, University of La Rochelle
-After decades of massive digitisation, an unprecedented amount of historical documents is available in digital
-format, along with their machine-readable texts. While this represents a major step forward with respect
-to preservation and accessibility, it also opens up new opportunities in terms of content mining and the
-next fundamental challenge is to develop appropriate technologies to efficiently search, retrieve and explore
-information from this ‘big data of the past’. Among semantic indexing opportunities, the recognition and
-classification of named entities are in great demand among humanities scholars. Yet, named entity recognition
-(NER) systems are heavily challenged with diverse, historical and noisy inputs. In this survey, we present the
-array of challenges posed by historical documents to NER, inventory existing resources, describe the main
-approaches deployed so far, and identify key priorities for future developments.
-CCS Concepts: •Computing methodologies →Information extraction ;Machine learning ;Language
-resources ;•Information systems →Digital libraries and archives.
-Additional Key Words and Phrases: named entity recognition and classification, historical documents, natural
-language processing, digital humanities
-1 INTRODUCTION
-For several decades now, digitisation efforts by cultural heritage institutions are contributing an
-increasing amount of facsimiles of historical documents. Initiated in the 1980s with small scale,
-in-house projects, the ‘rise of digitisation’ grew further until it reached, already in the early 2000s,
-a certain maturity with large-scale, industrial-level digitisation campaigns [ 188]. Billions of images
-are being acquired and, when it comes to textual documents, their content is transcribed either
-manually via dedicated interfaces, or automatically via optical character recognition (OCR) or
-handwritten text recognition (HTR) [ 31,129]. As a result, it is nowadays commonplace for memory
-institutions (e.g. libraries, archives, museums) to provide digital repositories that offer rapid, time-
-and location-independent access to facsimiles of historical documents as well as, increasingly,
-full-text search over some of these collections.
-Beyond this great achievement in terms of preservation and accessibility, the availability of
-historical records in machine-readable formats bears the potential of new ways to engage with their
-contents. In this regard, the application of machine reading to historical documents is potentially
-transformative, and the next fundamental challenge is to adapt and develop appropriate technolo-
-gies to efficiently search, retrieve and explore information from this ‘big data of the past’ [ 98]. Here
-research is stepping up and the interdisciplinary efforts of the digital humanities (DH), natural
-language processing (NLP) and computer vision communities are progressively pushing forward
-the processing of facsimiles, as well as the extraction, linking and representation of the complex
-Authors’ addresses: Maud Ehrmann, maud.ehrmann@epfl.ch, Ecole Polytechnique Fédérale de Lausanne; Ahmed Hamdi,
-ahmed.hamdi@univ-lr.fr, University of La Rochelle; Elvys Linhares Pontes, elvys.linhares_pontes@univ-lr.fr, University of
-La Rochelle; Matteo Romanello, matteo.romanello@epfl.ch, Ecole Polytechnique Fédérale de Lausanne; Antoine Doucet,
-antoine.doucet@univ-lr.fr, University of La Rochelle.arXiv:2109.11406v1  [cs.CL]  23 Sep 20212 Ehrmann et al.
-Fig. 1. Swiss journal L’Impartial , issue of 31 Dec 1918. Facsimile of the first page (left), zoom on an article
-(middle), and OCR of this article as provided by the Swiss National Library (completed in the 2010s) (right).
-information enclosed in transcriptions of digitised collections. In this endeavor, information extrac-
-tion techniques, and particularly named entity (NE) processing, can be considered among the first
-and most crucial processing steps.
-Named entity recognition and classification (NER for short) corresponds to the identification of
-entities of interest in texts, generally of the types Person ,Organisation andLocation . Such entities
-act as referential anchors which underlie the semantics of texts and guide their interpretation.
-Acknowledged some twenty years ago, NE processing has undergone major evolution since then,
-from entity recognition and classification to entity disambiguation and linking, and is representative
-of the evolution of information extraction from a document- to a semantic-centric view point [ 156].
-As for most NLP research areas, recent developments around NE processing are dominated by
-deep neural networks and the usage of embedded language representations [ 37,110]. Since their
-inception up to now, NE-related tasks are of ever-increasing importance and at the core of virtually
-any text mining application.
-From the NLP perspective, NE processing is useful first and foremost in information retrieval,
-or the activity of retrieving a specific set of documents within a collection given an input query.
-Guo et al. [ 78] as well as Lin et al. [ 118] showed that more than 70% of queries against modern
-search engines contain a named entity, and it has been suggested that more than 30% of content-
-bearing words in news text correspond to proper names [ 69]. Entity-based document indexing
-is therefore desirable. NEs are also highly beneficial in information extraction, or the activity of
-finding information within large volumes of unstructured texts. The extraction of salient facts about
-predefined types of entities in free texts is indeed an essential part of question answering [ 127],
-media monitoring [ 182], and opinion mining [ 9]. Besides, NER is helpful in machine translation [ 85],
-text summarisation [97], and document clustering [62], especially in a multilingual setting [181].
-As for historical material (cf. Figure 1), primary needs also revolve around retrieving documents
-and information, and NE processing is of similar importance [ 35]. There are less query logs over
-historical collections than for the contemporary web, but several studies demonstrate how prevalent
-entity names are in humanities users’ searches: 80% of search queries on the national library of
-France’s portal Gallica contain a proper name [ 33], and geographical and person names dominate
-the searches of various digital libraries, be they of artworks, domain-specific historical documents,
-historical newspapers, or broadcasts [ 14,32,92]. Along the same line, several user studies emphasise
-the role of entities in various phases of the information-seeking workflow of historians [ 47,71],
-now also reflected in the ‘must-have’ of exploration interfaces, e.g. as search facets over historicalNamed Entity Recognition and Classification on Historical Documents: A Survey 3
-newspapers [ 49,145] or as automatic suggestions over large-scale cultural heritage records [ 72].
-Besides document indexing, named entity recognition can also benefit downstream processes
-(e.g. biography reconstruction [ 64] or event detection [ 176]), as well as various data analysis and
-visualisation (e.g. on networks [ 194]). Finally, and perhaps most importantly, NER is the first step of
-entity linking, which can support the cross-linking of multilingual and heterogeneous collections
-based on authority files and knowledge bases. Overall, entity-based semantic indexing can greatly
-support the search and exploration of historical documents, and NER is increasingly being applied
-on such a material.
-Yet, the recognition and classification of NEs in historical texts is not straightforward, and
-performances are rarely on par with what is usually observed on contemporary, well-edited English
-news material [ 50]. In particular, NER on historical documents faces the challenges of domain
-heterogeneity, input noisiness, dynamics of language, and lack of resources. If some of these issues
-have already been tackled in isolation in other contexts (with e.g. user-generated text), what makes
-the task particularly difficult is their combination, as well as their magnitude: texts are severely
-noisy, domains and time periods are far apart, and there is no (or not yet) historical web to easily
-crawl to capture language models. In this context of new material, interests and needs, and in times
-of rapid technological change with deep learning, this paper presents a survey of NER research
-on historical documents. The objectives are to study the main challenges facing named entity
-recognition and classification when applied to historical documents, to inventory the strategies
-deployed to deal with them so far, and to identify key priorities for future developments.
-Section 2 outlines the objectives, the scope and the methodology of the survey, and Section 3
-provides background on NE processing. Next, Section 4 introduces and discusses the challenges of
-NER on historical documents. In response, Section 5 proposes an inventory of existing resources,
-while Section 6 and 7 present the main approaches, in general and in view of specific challenges,
-respectively. Finally, Section 8 discusses next priorities and concludes.
-2 FRAMING OF THE SURVEY
-2.1 Objectives
-This survey focuses on NE recognition and classification, and does not consider entity linking nor
-entity relation extraction. With the overall objective of characterising the landscape of NER on
-historical documents, the survey reviews the history, the development, and the current state of
-related approaches. In particular, we attempt to answer the following questions:
-Q1What are the key challenges posed by historical documents to NER?
-Q2Which existing resources can be leveraged in this task, and what is their coverage in terms
-of historical periods, languages and domains?
-Q3Which strategies were developed and successfully applied in response to the challenges faced
-by NER on historical documents? Which aspects of NER systems require adaptation in order
-to obtain satisfying performances on this material?
-While investigating the answers to these questions, the survey will also shed light on the variety of
-domains and usages of NE processing in the context of historical documents.
-2.2 Document Scope and Methodology
-Cultural heritage covers a wide range of material and the document scope of this survey, centred
-on ‘historical documents’, needed to be clearly delineated. From a document processing perspective,
-there is no specific definition of what a historical document is, but only shared intuitions based on
-multiple criteria. Time seems an obvious one, but where to draw the line between historical and
-contemporary documents is a tricky question. Other aspects include the digital origin (digitised or4 Ehrmann et al.
-Title Type Discipline
-Annual Meeting of the Association for Computational Linguistics (ACL) proceedings CL/NLP
-Digital Humanities conference proceedings DH
-Digital Scholarship in the Humanities (DSH) journal DH
-Empirical Methods in Natural Language Processing (EMNLP) proceedings NLP
-International Conference on Language Resources and Evaluation (LREC) proceedings NLP
-International Journal on Digital Libraries journal DH
-Journal of Data Mining and Digital Humanities (JDMDH) journal DH
-Journal on Computing and Cultural Heritage (JOCCH) journal DH
-Language Resources and Evaluation (LRE) journal NLP
-SIGHUM Workshop on Computational Linguistics for Cultural Heritage proceedings CL/NLP/DH
-Table 1. Publication venues whose archives were scanned as part of this survey (in alphabetical order).
-born-digital), the type of writing (handwritten, typeset or printed), the state of the material (heavily
-degraded or not), and of the language (historical or not). None of these criteria define a clear set of
-documents and any attempt of definition resorts to, eventually, subjective decisions.
-In this survey, we consider as historical document any document of textual nature mainly,
-produced or published up to 1979, regardless of its topic, genre, style or acquisition method. The
-year 1979 is not arbitrary and corresponds to one of the most recent ‘turning points’ acknowledged
-by historians [ 26]. This document scope is rather broad, and the question of the too far-reaching
-‘textual nature’ can be raised in relation to documents such as engravings, comics, card boards or
-even maps, which can also contain text. In practice, however, NER was mainly applied on printed
-documents so far, and these represent most of the material of the work reviewed here.
-The compilation of the literature was based on the following strategies: scanning of the archives
-of relevant journals and conference series, search engine-based discovery, and citation chaining.
-We considered key journals and conference series both in the fields of natural language processing
-and digital humanities (see Table 1). For searching, we used a combination of keywords over the
-Google Scholar and Semantic Scholar search engines.1With a few exceptions, we only considered
-publications that included a formal evaluation.
-2.3 Previous surveys and target audience
-Previous surveys on NER focused either on approaches in general, giving an overview of features,
-algorithms and applications, or on specific domains or languages. In the first group, Nadeau
-et al. [ 130] provided the first comprehensive survey after a decade of work on NE processing,
-reviewing existing machine learning approaches of that time, as well as typologies and evaluation
-metrics. Their survey remained the main reference until the introduction of neural network-based
-systems, recently reviewed by Yadav et al. [ 200] and Li et al. [ 116]. The latest NER survey to date
-is the one by Nazar et al. [ 131], which focuses specifically on generic domains and on relation
-extraction. In the second group, Leaman et al. [ 111] and Campos et al. [ 29] presented a survey
-of advances in biomedical named entity recognition, while Lei et al. [ 114] considered the same
-domain in Chinese. Shaalan focused on general NER in Arabic [175], and surveys exist for Indian
-languages [ 142]. Recently, Georgescu et al. [ 68] focused on NER aspects related to the cybersecurity
-domain. Turning our attention to digital humanities, Sporlerder [ 177] and Piotrowski [ 147] provided
-general overviews of NLP processing for cultural heritage domains, considering institutional,
-documentary and technical aspects. To the best of our knowledge, this is the first survey on the
-application of NER to historical documents.
-1E.g. ‘named entity recognition’, ‘nerc’, ‘named entity processing’, ‘historical documents’, ‘old documents’ over https:
-//scholar.google.com and https://www.semanticscholar.org/Named Entity Recognition and Classification on Historical Documents: A Survey 5
-The primary target audiences are researchers and practitioners in the fields of natural language
-processing and digital humanities, as well as humanities scholars interested in knowing and
-applying NER on historical documents. Since the focus is on adapting NER to historical documents
-and not on NER techniques themselves, this study assumes a basic knowledge of NER principles
-and techniques; however, it will provide information and guidance as needed. We use the terms
-‘historical NER’ and ‘modern NER’ to refer to work and applications which focus on, respectively,
-historical and non-historical (as we define them) materials.
-3 BACKGROUND
-Before delving into NER for historical documents, this section provides a generic introduction to
-named entity processing and modern NER (Section 3.1 and 3.2), to the types of resources required
-(Section 3.3), and to the main principles underlying NER techniques (Section 3.4).
-3.1 NE processing in general
-As of today, named entity tasks correspond to text processing steps of increasing level of complexity,
-defined as follows:
-(1)recognition and classification – or the detection of named entities, i.e. elements in texts
-which act as a rigid designator for a referent, and their categorisation according to a set of
-predefined semantic categories;
-(2)disambiguation/linking – or the linking of named entity mentions to a unique reference in a
-knowledge base, and
-(3) relation extraction – or the discovery of relations between named entities.
-First introduced in 1995 during the 6𝑡ℎMessage Understanding Conference [ 75], the task of NE
-recognition and classification (task 1 above) quickly broadened and became more complex, with
-the extension and refinement of typologies,2the diversification of languages taken into account,
-and the expansion of the linguistic scope with, along proper names, the consideration of pronouns
-and nominal phrases as candidate lexical units (especially during the ACE program [ 45]). Later
-on, as recognition and classification were reaching satisfying performances, attention shifted to
-finer-grained processing, with metonymy recognition [ 123] and fine-grained classification [ 57,122],
-and to the next logical step, namely entity resolution or disambiguation (task 2 above, not covered
-in this survey). Besides the general domain of clean and well-written news wire texts, NE processing
-is also applied to specific domains, particularly bio-medical [ 73,102], and to more noisy inputs such
-as speech transcriptions [ 66] and tweets [ 148,159]. In recent years, one of the major developments
-of NE processing is its application to historical material.
-Importantly, and although the question of the definition of named entities is not under focus here,
-we shall specify that we adopt in this regard the position of Nadeau et al. [ 130] for which “ the word
-‘Named’ aims to restrict [Named Entities] to only those entities for which one or many rigid designators,
-as defined by S. Kripke, stands for the referent ”. Concretely speaking, named entities correspond to
-different types of lexical units, mostly proper names and definite descriptions, which, in a given
-discourse and application context, autonomously refer to a predefined set of entities of interest.
-There is no strict definition of named entities, but only a set of linguistic and application-related
-criteria which, eventually, compose a heterogeneous set of units.3
-Finally, let us mention two NE-related specific research directions: temporal information process-
-ing and geoparsing. This survey does not consider work related to temporal analysis and, when
-relevant, occasionally mentions some related to geotagging.
-2See e.g. the overviews of Nadeau et al. [130, pp. 3-4] and Ehrmann et al. [51].
-3See Ehrmann [48, pp.81-188] for an in-depth discussion of NE definition.6 Ehrmann et al.
-Table 2. Illustration of IOB tagging scheme (example 1).
-Tokens (X) NER label (Y) POS Chunk
-Switzerland B-LOC NNP I-NP
-stands O VBZ I-VP
-accused O VBN I-VP
-by O IN I-PP
-Senator O NNP I-NP
-Alfonse B-PER NNP I-NP
-D’Amato I-PER NNP I-NP
-... ... ... ...
-3.2 NER in a nutshell
-3.2.1 A sequence labelling task. Named entity recognition and classification is defined as a sequence
-labelling task where, given a sequence of tokens, a system seeks to assign labels (NE classes) to this
-sequence. The objective for a system is to observe, in a set of labelled examples, the word-labels
-correspondences and their most distinctive features in order to learn identification and classification
-patterns which can then be used to infer labels for new, unseen sequences of tokens. This excerpt
-from the CoNLL-03 English test dataset [ 190] illustrates a training example (or the predictions a
-system should output):
-(1)[𝐿𝑂𝐶 Switzerland] stands accused by Senator [𝑃𝐸𝑅Alfonse D’Amato], chairman of the powerful [𝑂𝑅𝐺
-U.S. Senate Banking Committee], of agreeing to give money to [𝐿𝑂𝐶 Poland] (...)
-Such input is often represented with the IOB tagging scheme, where each token is marked as being
-at the beginning (B), inside (I) or outside (O) of an entity of a certain class [ 155]. Fig. 2 represents
-the above example in IOB format, from which systems try to extract features to learn NER models.
-3.2.2 Feature space. NER systems’ input corresponds to a linear representation of text as a sequence
-of characters, usually processed as a sequence of words and sentences. This input is enriched with
-features or ‘clues’ a system consumes in order to learn (or generalise) a model. Typical NER
-features may be observed at three levels: words, close context or sentences, and document. At the
-morphological level, features include e.g. the word itself, its length, whether it is (all) capitalised or
-not, whether it contains specific word patterns or specific affixes (e.g. the suffixes -vitch or-sson
-for person names in Russian and Swedish), its base form, its part of speech (POS), and whether
-it is present in a predefined list. At the contextual level, features reflect the presence or absence
-of surrounding ‘trigger words’ (or combination thereof, e.g. Senator andtopreceding a person or
-location name, Committee ending an organisation name), or of surrounding NE labels. Finally, at
-the document level, features correspond to e.g. the position of the mention in the document or
-paragraph, the occurrence of other entities in the document, or the document metadata.
-These features can be absent or ambiguous, and none of them is systematically reliable; it is
-therefore necessary to combine them, and this is where statistical models are helpful. Features are
-observed in positive and negative examples, and are usually also encoded according to the IOB
-scheme (e.g. part-of-speech and chunk annotation columns in Fig. 2). In traditional, feature-based
-machine learning, features are specified by the developer (feature engineering), while in deep
-learning they are learned by the system itself (feature learning) and go beyond those specified
-above.
-3.2.3 NER evaluation. Systems are evaluated in terms of precision (P), recall (R) and F-measure
-(F-score, the harmonic mean of P and R). Over the years, different scoring procedures and measuresNamed Entity Recognition and Classification on Historical Documents: A Survey 7
-were defined in order to take into account various phenomena such as partial match or incorrect
-type but correct mention, or to assign different weights to various entity and/or error types. These
-fine-grained evaluation metrics allow for a better understanding of the system’s performance
-and for tailoring the evaluation to what is relevant for an application. Examples include the
-(mostly abandoned) ACE ‘entity detection and recognition value’ (EDR), the slot error rate (SER) or,
-increasingly, the exact vs. fuzzy match settings where entity mention boundaries need to correspond
-exactly vs. to overlap with the reference. We refer the reader to [ 130, pp.12-15], [ 116, pp.3-4] and
-[136, chapter 6]. This survey reports systems’ performances in terms of P, R and F-score.
-3.3 NER resource types
-Resources are essential when developing NER systems. Four main types of resources may be
-distinguished, each playing a specific role.
-3.3.1 Typologies. Typologies define a semantic framework for the entities under consideration.
-They corresponds to a formalised and structured description of the semantic categories to consider
-(the objects of the world which are of interest), along with a definition of their scope (their realisation
-in texts). There exist different typologies, which can be multi-purpose or domain-specific, and with
-various degrees of hierarchisation. Most of them are defined and published as part of evaluation
-campaigns, with no tradition of releasing typologies as such outside this context. Typologies form
-the basis of annotation guidelines, which explicit the rules to follow when manually annotating a
-corpus and are crucial for the quality of the resulting material.
-3.3.2 Lexicons and knowledge bases. Next, lexicons and knowledge bases provide information
-about named entities which may be used by systems for the purposes of recognition, classification
-and disambiguation. This type of resource has evolved significantly in the last decades, as a result
-of the increased complexity of NE-related tasks and of technological progress made in terms of
-knowledge representation. Information about named entities can be of lexical nature, relating to the
-textual units making up named entities, or of encyclopædic nature, concerning their referents. The
-first case corresponds to simple lists named lexica or ‘gazetteers’4which encode entity names, used
-in look-up procedures, and trigger words, used as features to guess names in texts. The second case
-corresponds to knowledge bases which encode various non-linguistic information about entities
-(e.g. date of birth/death, alma mater, title, function), used mainly for entity linking (Wikipediaand
-DBpedia [ 113] being amongst the best-known examples). With the advent of neural language
-models, the use of explicit lexical information stored in lexica could have been definitely sealed,
-however gazetteer information still proves useful when incorporated as feature concatenated to
-pre-trained embeddings [37, 89], confirming that NER remains a knowledge-intensive task [157].
-3.3.3 Word embeddings and language models. Word embeddings are low-dimensional, dense vec-
-tors which represent the meaning of words and are learned from word distribution in running
-texts. Stemming from the distributional hypothesis, they are part of the representation learning
-paradigm where the objective is to equip machine learning algorithms with generic and efficient
-data representations [ 16]. Their key advantage is that they can be learned in a self-supervised fash-
-ion, i.e. from unlabelled data, enabling the transition from feature engineering to feature learning.
-The principle of learning and using distributional word representations for different tasks was
-already present in [ 13,37,193], but it is with the publication of word2vec, a software package which
-provided an efficient way to learn word embeddings from large corpora [ 126], that embeddings
-started to become a standard component of modern NLP systems, including NER.
-4A term initially devoted to toponyms afterwards extended to any NE type.8 Ehrmann et al.
-Since then, much effort has been devoted to developing effective means of learning word rep-
-resentations, first moving from words to sub-words and characters, and then from words to
-words-in-context with neural language models. The first generation of ‘traditional’ embeddings
-corresponds to static word embeddings where a single representation is learned for each word
-independently of its context (at the type level). Common algorithms for such context-independent
-word embeddings include Google word2vec [ 126], Stanford Glove [ 143] and SENNA [ 37]. The
-main drawbacks of such embeddings are their poor modelling of ambiguous words (embeddings
-are static) and their inability to handle out-of-vocabulary (OOV) words, i.e. words not present
-in the training corpus and for which there is no embedding. The usage of character-based word
-embeddings, i.e. word representations based on a combination of its character representations, can
-help process OOV words and make better use of morphological information. Such representations
-can be learned in a word2vec fashion, as with fastText [ 21], or via CNN or RNN-based architectures
-(see Section 3.4 for a presentation of types of networks).
-However, even enriched with sub-word information, traditional embeddings are still ignorant of
-contextual information. This short-coming is addressed by a new generation of approaches which
-takes as learning objective language modelling, i.e. the task of computing the probability distribution
-of the next word (or character) given the sequence of previous words (or characters) [ 17]. By taking
-into account the entire input sequence, such approaches can learn deeper representations which
-capture many facets of language, including syntax and semantics, and are valid for various linguistic
-contexts (at the token level). They generate powerful language models (LMs) which can be used for
-downstream tasks and from which contextual embeddings can be derived. These LMs can be at the
-word level (e.g. ELMo [ 144], ULMFiT [ 88], BERT [ 43] and GPT [ 153]), or character-based such as
-the contextual string embeddings proposed by Akbik et al. [ 4] (a.k.a flair embeddings). Overall,
-alongside static character-based word and word embeddings, character-level and word-level LM
-embeddings are pushing the frontiers in NLP and are becoming key elements of NER systems, be it
-for contemporary or historical material.
-3.3.4 Corpora. Finally, a last type of resource essential for developing NER systems is labelled
-documents and, to some extent, unlabelled textual data. Labelled corpora illustrate an objective
-and are used either as a learning base or as a point of reference for evaluation purposes. Unlabelled
-textual material is necessary to acquire embeddings and language models.
-3.4 NER methods
-Similarly to other NLP tasks, NER systems are developed according to three standard families of
-algorithms, namely rule-based, feature-based (traditional machine learning) and neural-based (deep
-learning).
-3.4.1 Rule-based approaches. Early NER methods in the mid-1990s were essentially rule-based.
-Such approaches rely on rules manually crafted by a developer (or linguist) on the base of regularities
-observed in the data. Rules manipulate language as a sequence of symbols and interpret associated
-information. Organised in what makes up a grammar, they often rely on a series of linguistic pre-
-processing (sentence splitting, tokenization, morpho-syntactic tagging), require external resources
-storing language information (e.g. triggers words in gazetteers) and are executed using transducers.
-Such systems have the advantages of not requiring training data and of being easily interpretable,
-but need time and expertise for their design.
-3.4.2 Machine-learning based approaches. Very popular until the late 1990s, rule-based approaches
-were superseded by traditional machine learning approaches when large annotated corpora became
-available and allowed the machine learning of statistical models in supervised, semi-supervised, andNamed Entity Recognition and Classification on Historical Documents: A Survey 9
-later unsupervised fashion. Traditional, feature-based machine learning algorithms learn inductively
-from data on the base of manually selected features. In supervised NER, they include support vector
-machines [ 94], decision trees [ 185], as well as probabilistic sequence labelling approaches with
-generative models such as hidden markov models [ 19] and discriminative ones such as maximum
-entropy models [ 15] and linear-chained conditional random fields (CRFs) [ 109]. Thanks to their
-capacity to take into account the neighbouring tokens, CRFs proved particularly well-suited for
-NER tagging and became the standard for feature-based NER systems.
-3.4.3 Deep learning approaches. Finally, latest research on NER is largely (if not exclusively) domi-
-nated by deep learning (DL). Deep learning systems correspond to artificial neural networks with
-multiple processing layers which learn representations of data with multiple levels of abstrac-
-tion [ 112]. In a nutshell, (deep) neural networks are composed of computational units, which take
-a vector of input values, multiply it by a weight vector, add a bias, apply a non-linear activation
-function, and produce a single output value. Such units are organised in layers which compose
-a network, where each layer receives its input from the previous one and passes it to the next
-(forward pass), and where parameters that minimise a loss function are learned with gradient
-descent (backward pass). The key advantage of neural networks is their capacity to automatically
-learn input representations instead of relying on manually elaborated features, and very deep
-networks (with many hidden layers) are extremely powerful in this regard.
-Deep learning architectures for sequence labelling have undergone rapid change over the last few
-years. These developments are function of two decisive aspects for successful deep learning-based
-NER: at the architecture level, the capacity of a network to efficiently manage context, and, at the
-input representation level, the capacity to benefit from or learn powerful embeddings or language
-models. In what follows we briefly review main deep learning architectures for modern NER and
-refer the reader to Lin et al. [116] for more details.
-Motivated by the desire to avoid task-specific engineering as much as possible, Collobert et al. [ 37]
-pioneered the use of neural nets for four standard NLP tasks (including NER) with convolutional
-neural networks (CNN) that made used of trained type-level word embeddings and were learned in
-an end-to-end fashion. Their unified architecture SENNA5reached very competitive results for
-NER ( 89 .86%F-score on the CoNLL-03 English corpus) and near state-of-the-art results for the
-other tasks. Following Collobert’s work, developments focused on architectures capable of keeping
-information of the whole sequence throughout hidden layers instead of relying on fixed-length
-windows. These include recurrent neural networks (RNN), either simple [ 59] or bi-directional [ 170]
-(where input is processed from right to left and from left to right), and their more complex variants
-of long short-term memory networks (LSTM) [ 86] and gated recurrent units (GRU) [ 34] which
-mitigate the loss of distant information often observed in RNN. Huang et al. [ 89] were among the
-first to apply a bidirectional LSTM (BiLSTM) network with a CRF decoder to sequence labelling,
-obtaining 90 .1%F-score on the NER CoNLL-03 English dataset. Soon, BiLSTM networks became
-the de facto standard for context-dependent sequence labelling, giving rise to a body of work
-including Lample et al. [ 110], Chiu et al. [ 110], and Ma et al. [ 121] (to name but a few). Besides
-making use of bidirectional variants of RNN, these work also experiment with various input
-representations, in most cases combining learned character-based representations with pre-trained
-word embeddings. Character information has proven useful for inferring information for unseen
-words and for learning morphological patterns, as demonstrated by the 91 .2%F-score of Ma et
-al. [121] on CoNLL-03, and the systematically better results of Lample et al. [ 110] on the same
-dataset when using character information. A more recent study by Taillé et al. [ 186] confirms the
-role of sub-word representations for unseen entities.
-5‘Semantic/syntactic Extraction using a Neural Network Architecture’.10 Ehrmann et al.
-The latest far-reaching innovation in the DL architecture menagerie corresponds to self-attention
-networks, or transformers [ 196], a new type of simple networks which eliminates recurrence and
-convolutions and are based solely on the attention mechanism. Transformers allow for keeping a
-kind of global memory of the previous hidden states where the model can choose what to retrieve
-from (attention), and therefore use relevant information from large contexts. They are mostly trained
-with a language modelling objective and are typically organised in transformer blocks, which can be
-stacked and used as encoders and decoders. Major pre-training transformer architectures include the
-Generative Pre-trained Transformer (GPT, a left-to-right architecture) [ 153] and the Bidirectional
-Encoder Representation from Transformer (BERT, a bidirectional architecture) [ 43], which achieves
-92 .8%NER F-score on CoNLL-03. More recently, Yamada et al. [ 201] proposed an entity-aware
-self-attention architecture which achieved 94 .3%F-score on the same dataset. Transformer-based
-architectures are the focus of extensive research and many model variants were proposed, of which
-Tay et al. [187] propose an overview.
-Overall, two points should be noted. First, that beyond the race for the leader board (based on the
-fairly clean English CoNLL-03 dataset), pre-trained embeddings and language models play a crucial
-role and are becoming a new paradigm in neural NLP and NER (the ‘NLP’s ImageNet moment’ [ 167]).
-Second, that powerful language models are also paving the way for transfer learning, a method
-particularly useful with low-resource languages and out-of-domain contexts, as is the case with
-challenging, historical texts.
-4 CHALLENGES
-Named entity recognition on historical documents faces four main challenges for which systems
-developed on contemporary datasets are often ill-equipped. Those challenges are intrinsic to the
-historical setting, like time evolution and types of documents, and endemic to the text acquisition
-process, like OCR noise. This translates into a variable and sparse feature space, a situation com-
-pounded by the lack of resources. This section successively considers the challenges of document
-type and domain variety, noisy input, dynamics of language, and lack of resources.
-4.1 The (historical) variety space
-First, NER on historical texts corresponds to a wide variety of settings, with documents of different
-types (e.g. administrative documents, media archives, literary works, documentation of archival
-sites or art collections, correspondences, secondary literature), of different nature (e.g. articles,
-letters, declarations, memoirs, wires, reports), and in different languages, which, moreover, spans
-different time periods and encompasses various domains and countless topics. The objective here
-is not to inventory all historical document types, domains and topics, but to underline the sheer
-variety of settings which, borrowing an expression from B. Plank [ 149], compose the ‘variety space’
-NLP is confronted with, intensified in the present case by the time dimension.6
-Two comments should be made in connection with this variety. First, domain shift is a well-
-known issue for NLP systems in general and for modern NER in particular. While B. Plank [ 149]
-and J. Einsenstein [ 56] investigated what to do about bad and non-standard (or non-canonical)
-language with NLP in general, Augenstein et al. [ 8] studied the ability of modern NER systems
-to generalise over a variety of genres, and Taillé et al. [ 186] over unseen mentions. Both studies
-demonstrated a NER transfer gap between different text sources and domains, confirming earlier
-findings of Vilain et al. [ 197]. While no studies have (yet) been conducted on the generalisation
-6Considering there is no common grounds on what constitutes a domain and that the term is overloaded, Plank proposes
-the concept of “variety space”, defined as a “ unknown high-dimensional space, whose dimensions contain (fuzzy) aspects such
-as language (or dialect), topic or genre, and social factors (age, gender, personality, etc.), amongst others. A domain forms a
-region in this space, with some members more prototypical than others ” [149].Named Entity Recognition and Classification on Historical Documents: A Survey 11
-capacities of NER systems within the realm of historical documents, there are strong grounds to
-believe that systems are equally impacted when switching domain and/or document type.
-Second, this (historical) variety space is all the more challenging as the scope of needs and
-applications in humanities research is much broader than the one usually addressed in modern
-NLP. For sure the variety space does not differ much between today and yesterday’s documents (i.e.
-if we were NLP developers living in the 18C we would be more or less confronted with the same
-‘amount’ of variety as today), however here the difference lies in the interest for all or part of this
-variety: while NLP developments tend to focus on some well-identified and stable domains/sub-
-domains (sometimes motivated by commercial opportunities), the (digital) humanities and social
-sciences research communities are likely interested in the whole spectrum of document types and
-domains. In brief, if the magnitude of the variety space is more or less similar for contemporary and
-historical documents, the range of interests and applications in humanities and cultural heritage
-requires—almost by design—the consideration of an expansive array of domains and document
-types.
-4.2 Noisy input
-Next, historical NER faces the challenges of noisy input derived from automatic text acquisition
-over document facsimiles. Text is acquired via two processes: 1) optical character recognition (OCR)
-and handwritten text recognition (HTR), which recognise text characters from images of printed
-and handwritten documents respectively, and 2) optical layout recognition (OLR), which identifies,
-orders and classifies text regions (e.g. paragraph, column, header). We consider both successively.
-4.2.1 Character recognition. The OCR transcription of the newspaper article on the right-hand
-side of Figure 1 illustrates a typical, mid-level noise, with words perfectly readable ( la Belgique ),
-others illegible ( pu. s >s « _jnces ), and tokenization problems ( n’à’pas ,le’Conseiller ). While this does
-not really affect human understanding when reading, the same is not true for machines which
-face numerous OOV words. Be it by means of OCR or HTR, text acquisition performances can be
-impacted by several factors, including: a) the quality of the material itself, affected by the poor
-preservation and/or original state of documents with e.g. ink bleed-through, stains, faint text, and
-paper deterioration; b) the quality of the scanning process, with e.g. an inadequate resolution or
-imaging process leading to frame or border noise, skew, blur and orientation problems; or c) as per
-printed documents and in absence of standardisation, the diversity of typographic conventions
-through time including e.g. varying fonts, mixed alphabets but also diverse shorthand, accents
-and punctuation. These difficulties naturally challenge character recognition algorithms which are,
-what is more, evolving from one OCR campaign to another, usually conducted at different times by
-libraries and archives. As a result, not only the transcription quality is below expectations, but the
-type of noise present in historical machine-readable corpora is also very heterogeneous.
-Several studies investigated the impact of OCR noise on downstream NLP tasks. While Lo-
-presti [ 120] demonstrated the detrimental effect of OCR noise propagation through a typical NLP
-pipeline on contemporary texts, Van Strien et al. [195] focused on historical material and found a
-consistent impact of OCR noise on the six NLP tasks they evaluated. If sentence segmentation and
-dependency parsing bear the brunt of low OCR quality, NER is also affected with a significant drop
-of F-score between good and poor OCR (from 87%to63%for person entities). Focusing specifically
-on entity processing, Hamdi et al. [ 79,80] confronted a BiLSTM-based NER model with OCR outputs
-of the same text but of different qualities and observed a 30 percentage point loss in F-score when
-the character error rate increased from 7% to 20%. Finally, in order to assess the impact of noisy
-entities on NER during the CLEF-HIPE-2020 NE evaluation campaign on historical newspapers12 Ehrmann et al.
-(HIPE-2020 for short),7Ehrmann et al. [ 53] evaluated systems’ performances on various entity
-noise levels, defined as the length-normalised Levenshtein distance between the OCR surface form
-of an entity and its manual transcription. They found remarkable performance differences between
-noisy and non-noisy mentions, and that already as little noise as 0.1 severely hurts systems’ abilities
-to predict an entity and may halve their performances. To sum up, whether focused on a single
-OCR version of text(s) [ 195], on different artificially-generated ones [ 79], or on the noise present in
-entities themselves [ 53], these studies clearly demonstrate how challenging OCR noise is for NER
-systems.
-4.2.2 Layout recognition. Beside incorrect character recognition, textual input quality can also be
-affected by faulty layout recognition. Two problems surface here. The first relates to incorrect page
-region segmentation which mixes up text segments and produces, even with correct OCR, totally
-unsuitable input (e.g. a text line reading across several columns). Progress in OLR algorithms makes
-this problem rarer, but it is still present for collections processed more than a decade ago. The
-second has to do with the unusual text segmentation resulting from correct OLR of column-based
-documents, with very short line segments resulting in numerous hyphenated words (cf. Figure 1).
-The absence of proper sentence segmentation and word tokenization also affects performances, as
-demonstrated in HIPE-2020, in particular Boros et al [ 25], Ortiz Suárez et al . [137] and Todorov et
-al. [191] (see Section 6.3).
-Overall, OCR and OLR noises lead to a sparser feature space which greatly affects NER perfor-
-mances. What makes this ‘noisiness’ particularly challenging is its wide diversity and range: an
-input can be noisy in many different ways, and be little to very noisy. Compared to social media,
-for which Baldwin et al. [ 10] demonstrated that there exists a noise similarity from a medium to
-another (blog, Twitter, etc.) and that this noise is mostly ‘NLP-tractable’, OCR and OLR noises in
-historical documents appear as real moving targets.
-4.3 Dynamics of language
-Another challenge relates to the effects of time and the dynamics of language. As a matter of fact,
-historical languages exhibit a number of differences with modern ones, having an impact on the
-performances of NLP tools in general, and of NER in particular [147].
-4.3.1 Historical spelling variations. The first source of difficulty relates to spelling variations across
-time, due either to the normal course of language evolution or to more prescriptive orthographic
-reforms. For instance, the 1740 edition of the dictionary of the French Academy (which had 8
-editions between 1694 and 1935) introduced changes in the spelling of about one third of the French
-vocabulary and, in Swedish 19C literary texts, the letters <f/w/e/q> were systematically used instead
-of <v/v/ä/k> in modern Swedish [ 23]. NER can therefore be affected by poor morpho-syntactic
-tagging over such morphological variety, and by spelling variation of trigger words and of proper
-names themselves. While the latter are less affected by orthographic reforms, they do vary through
-time [23].
-4.3.2 Naming conventions. Changes in naming conventions, particularly for person names, can also
-be challenging. Let alone the numerous aristocratic and military titles that were used in people’s
-addresses, it was, until recently, quite common to refer to a spouse using the name of her husband
-(which affects more the linking than recognition), and to use now outdated addresses, e.g. the
-French expression sieur . These changes have been studied by Rosset et al. [ 165] who compared the
-structure of entity names in historical newspapers vs. in contemporary broadcast news. Differences
-7https://impresso.github.io/CLEF-HIPE-2020/Named Entity Recognition and Classification on Historical Documents: A Survey 13
-include the prevalence of the structure title + last name vs.first + last name forPerson in historical
-newspapers and contemporary broadcast news respectively, and of single-component names vs.
-multiple-component names for Organisation (idem). Testing several classifiers, the authors also
-showed that it is possible to predict the period of a document from the structure of its entities,
-thus confirming the evolution of names over time. For their part, Lin et al. [ 117] studied the
-generalisation capacities of a state-of-the-art neural NER system on entities with weak name
-regularity in a modern corpus and concluded that name regularity is critical for supervised NER
-models to generalise over unseen mentions.
-4.3.3 Entity and context drifts. Finally, a further complication comes from the historicity of entities,
-also known as entity drift, with places, professions, and types of major entities fading and emerging
-over time. For instance, a large part of profession names, which can be used as clues to recognise
-persons, has changed from the 19C to the 21C.8This dynamism is still valid today (NEs are an open
-class) and its characteristics as well as its impact on performances is particularly well documented
-for social media: Fromreide et al. showed a loss of 10 F-score percentage points between two Twitter
-corpora sampled two years apart [ 65], and Derczynski et al. systematised the analysis with the
-W-NUT2017 shared task on novel and emerging entities where, on training and test sets with very
-little entity overlaps, the maximum F-score was only 40%[42]. Besides confirming some degree of
-‘artificiality’ of classical NE corpora where the overlap between mentions in the train and the test
-sets do not reflect real-life settings, these studies illustrate the poor generalisation capacities of
-NER systems to unseen mentions due to time evolution. How big and how quick is entity drift in
-historical corpora? We could not find any quantitative study on this, but a high variability of the
-global referential frame through time is more than likely.
-Overall, the dynamics of language represent a multi-faceted challenge where the disturbing factor
-is not anymore an artificially introduced noise like with OCR and OLR, but the naturally occurring
-alteration of the signal by the effects of time. Both phenomena result in a sparser feature space,
-but the dynamics of language appear less elusive and volatile than OCR. Compared to OCR noise,
-its impact on NER performances is however relatively under-studied, and only a few diachronic
-evaluations were conducted on historical documents so far. Worth of mention is the evaluation
-of several NER systems on historical newspaper corpora spanning ca. 200 years, first with the
-study of Ehrmann et al. [ 50], second on the occasion of the HIPE-2020 shared task [ 53]. Testing the
-hypothesis of the older the document, the lower the performance, both studies reveal a contrasted
-picture with non-linear F-score variations over time. If a clear trend of increasing recall over time
-can be observed in [ 50], further research is needed to distinguish and assess the impact of each of
-the aforementioned time-related variations.
-4.4 Lack of resources
-Finally, the three previous challenges are compounded by a fourth one, namely a severe lack of
-resources. As mentioned in Section 3.3, the development of NER systems relies on four types of
-resources—typologies, lexicons, embeddings and corpora—which are of particular importance for
-the adaptation of NER systems to historical documents.
-With respect to typologies, the issue at stake is, not surprisingly, their dependence on time
-and domain. While mainstream typologies with few ‘universal’ classes (e.g. Person ,Organisation ,
-Location , and a few others) can for sure be re-used for historical documents, this obviously does not
-mean that they are perfectly suited to the content or application needs of any particular historical
-collection. Just as universal entity types cannot be used in all contemporary application contexts,
-8See for example the variety of occupations in the HISCO database: iisg.amsterdam/en/data/data-websites/history-of-work14 Ehrmann et al.
-neither can they be systematically applied to all historical documents: only a small part can be
-reused, and they require adaptation. An example is warships, often mentioned in 19C documents,
-for which none of the mainstream typologies has an adequate class. To say that typologies need
-to be adapted is almost a truism, but it is worth mentioning for it implies that the application of
-off-the-shelf NER tools–as is often done–is unlikely to capture all entities of interest in a specific
-collection and, therefore, is likely to penalise subsequent studies.
-Besides the (partial) inadequacy of typologies, the lack of annotated corpora severely impedes the
-development of NER systems for historical documents, for both training and evaluation purposes.
-While unsupervised domain adaptation approaches are gaining interest [ 154], most methods still
-depend on labelled data to train their models. Little training data usually results in inferior perfor-
-mances, as demonstrated—if proof were needed—by Augenstein et al. for NER on contemporary
-data [ 8, p. 71], and by Ehrmann et al. on historical newspapers [ 53, Section 7]. NE-annotated
-historical corpora exist, but are still rare and scattered over time and domains (cf. Section 5). This
-paucity also affects systems’ evaluation and comparison which, besides the lack of gold standards,
-is also characterised by fragmented and non-standardised evaluation approaches. The recently
-organised CLEF-HIPE-2020 shared task on NE processing in multilingual and historical newspapers
-is a first step towards alleviating this situation [53].
-Last but not least, if large quantities of textual data are being produced via digitisation, several
-factors slow down their dissemination and usage as base material to acquire embeddings and
-language models. First, textual data is acquired via a myriad of OCR softwares which, despite the
-definition of standards by libraries and archives, supply quite disparate and heavy-to-process output
-formats [ 52,164]. Second, even when digitised, historical collections are not systematically openly
-accessible due to copyright restrictions. Despite the recent efforts and the growing awareness of
-cultural institutions of the value of such assets for machine learning purposes [ 139], these factors
-still hamper the learning of language representations from large amounts of historical texts.
-Far from being unique to historical NER, lack of resources is a well-known problem in modern
-NER [ 51], and more generally in NLP [ 96]. In the case at hand, the lack of resources is exacerbated
-by the somewhat youth of the research field and the relatively low attention towards the creation of
-resources compared to other domains. Moreover, considering how wide is the spectrum of domains,
-languages, document types and time periods to cover, it is likely that a certain resource sparsity will
-always remain. Finding ways to mitigate the impact of the lack of resources on system development
-and performances is thus essential.
-Conclusion on challenges . NER on historical documents faces four main challenges, namely
-historical variety space, noisy input, dynamics of language, and lack of resources. If none of
-these challenges is new per se—which does not lessen their difficulty—, what makes the situation
-particularly challenging is their combination, in what could somehow be qualified an ‘explosive
-cocktail’. This set of challenges has two main characteristics: first, the prevalence of the time
-dimension, which not only affects language and OCR quality but also causes domain and entity
-drifts; and, second, the intensity of the present difficulties, with OCR noise being a real moving
-target, and domains and (historical) languages being highly heterogeneous. As a result, with
-feature sparsity adding up to multiple confounding factors, systems’ learning capacities are severely
-affected. NER on historical documents can therefore be cast as a domain and time adaptation
-problem, where approaches should be robust to non-standard, historical inputs, what is more in
-a low-resource setting. A first step towards addressing these challenges is to rely on appropriate
-resources, discussed in the next section.Named Entity Recognition and Classification on Historical Documents: A Survey 15
-5 RESOURCES FOR HISTORICAL NER
-This section surveys existing resources for historical NER, considering typologies and annotation
-guidelines, annotated corpora, and language representations (see Section 3.3 for a presentation of
-NER resource types). Special attention is devoted to how these resources distribute over languages,
-domains and time periods, in order to highlight gaps that future efforts should attempt to fill.
-5.1 Typologies and annotation guidelines
-Typologies and annotation guidelines for modern NER cover primarily the general and bio-medical
-domains, and the most used ones such as MUC [ 76], CoNLL [ 190], and ACE [ 45] consist mainly of
-a few high-level classes with the ‘universal’ triad Person ,Organisation andLocation [51]. Although
-they are used in various contexts, they do not necessarily cover the needs of historical documents.
-To the best of our knowledge, very few typologies and guidelines designed for historical material
-were publicly released so far. Exceptions include the Quaero [ 165,166], SoNAR [ 125] and impresso
-(used in HIPE-2020) [ 54] typologies and guidelines adapted or developed for historical newspapers
-in French, German, and English. Designing guidelines and effectively annotating NEs in historical
-documents is not as easy as it sounds and peculiarities of historical texts must be taken into account.
-These include for example OCRed text, with the question of how to determine the boundaries
-of mentions in gibberish strings, and historical entities, with the existence of various historical
-statuses of entities through times (e.g. Germany has 8 Wikidata IDs over the 19C and 20C [ 55,
-pp.9-10]).
-5.2 Annotated corpora
-Annotated corpora correspond to sets of documents manually or semi-automatically tagged with
-NEs according to a given typology, and are essential for the development and evaluation of NER
-systems (see Section 3.3). This section inventories NE-annotated historical corpora documented
-in publications and released under an open license.9Their presentation is organised into three
-broad groups (‘news’, ‘literature(s)’ and ‘other’), where they appear in alphabetical order. Unless
-otherwise noted, all corpora consist of OCRed documents.
-Let us start with some observations on the general picture. We could inventory 17 corpora, whose
-salient characteristics are summarised in Table 3. It is worth noting that collecting information
-about released corpora is far from easy and that our descriptions are therefore not homogeneous. In
-terms of language coverage, the majority of corpora are monolingual, and less than a third include
-documents written in two or more languages. Overall, these corpora provide support for eleven
-currently spoken languages and two dead languages (Coptic and Latin). With respect to corpus
-size, the number of entities appears as the main proxy and we distinguish between small (< 10k),
-medium (10-30k), large (30-100k) and very large corpora (> 100k).10In the present inventory, very
-large corpora are rather exceptional; roughly one third of them are small-sized, while the remaining
-are medium- or large-sized corpora. Next, and not surprisingly, a wide spectrum of domains is
-represented, from news to literature. This tendency towards domain specialisation is also reflected
-in typologies with, alongside the ubiquitous triad of Person ,Location , and Organisation types, a
-long tail of specific types reflecting the information or application needs of particular domains.
-Finally, in terms of time periods covered, we observe a high concentration of corpora in the 19C,
-directly followed by 20C and 21C, while corpora for previous centuries are either scarce or absent.
-9Inventory as of June 2021. The Voices of the Great War corpus [ 27] is not included for not released under an open license.
-10For comparison, the CoNLL-03 dataset contains ca. 70k mentions for English and 20k for German [ 190], while OntoNotes
-v5.0 contains 194k mentions for English, 130k for Chinese and 34k for Arabic [151].16 Ehrmann et al.
-Corpus Doc. type Time period Tag set Lang. # NE s Size License
-Quaero Old Press [165] newspapers 19C Quaero fr 147,682 xl elra
-Europeana [132] newspapers 19C per,loc,org fr, de, nl 40,801 l cc0
-De Gasperi [180] various types 20C per,gpe it 35,491 l cc by-nc-sa
-Latin NER [60] literary texts 1C bce-2C per,geo,grp la 7,175 s gpl v3.0
-HIMERA [189] medical lit. 19C-21C custom en 8,400 s cc by
-Venetian references [36] publications 19C-21C custom Multi 12,879 m cc by
-Finnish NER [169] newspapers 19C-20C per,loc,org fi 26,588 m n/a
-droc [106] novels 17C-20C custom de 6,013 s cc by
-Travel writings [178] travelogues 19C-20C loc en 2,228 sn/a
-Czech Hist. NE Corpus [90] newspapers 19C custom cz 4,017 s cc by-nc-sa
-LitBank [12] novels 19C-20C ace(w/o wea) en 14,000 l cc by-sa
-BIOfid [2] publications 18C-20C extended GermEval de 33,545 l gpl v3.0
-HIPE [55] newspapers 18C-21C impresso de, en, fr 19,848 m cc by-nc-sa
-BDCamões [74] literary texts 16C-21C custom pt 144,600 xl cc by-nc-nd
-Coptic Scriptorium corpora literary texts 3C-5C custom cop 88,068 l cc by
-GeoNER [104] literary texts 16C-17C geo fr 264 s lgpl-lr
-NewsEye [81] newspapers 19C-20C impresso -comp. de, fr, fi,s v 30,580 l cc by
-Table 3. Overview of reviewed NE-annotated historical corpora (ordered by publication year).
-5.2.1 News. The first group brings together corpora built from historical newspaper collections.
-With corpora in five languages (Czech, Dutch, English, French and German), news emerges as the
-best-equipped domain in terms of labelled data availability.
-The Czech Historical NE Corpus [ 91] is a small corpus produced out of the year 1872 of the
-Czech title Posel od Čerchova . Articles are annotated according to six entity types—persons, institu-
-tions, artifacts & objects, geographical names, time expressions and ambiguous entities—which,
-despite being custom, bear substantial similarities with major typologies. The corpus was manually
-annotated by two annotators with an inter-annotator agreement (IAA) of 0.86 (Cohen’s Kappa).
-Europeana NER corpora11[132] is a large-sized collection of NE-annotated historical newspaper
-articles in Dutch, French and German, containing primarily 19C materials. These corpora were
-sampled from the Europeana newspaper collection [ 133] by randomly selecting 100 pages from all
-titles for each language, considering only pages with a minimum word-level accuracy of 80%. Three
-entity types were considered (person, location, organisation), yet no IAA for the annotations is
-reported. Instead, the quality and usefulness of these annotated corpora were assessed by training
-and evaluating the Stanford CRF NER classifier (see Section 3.4.2).
-The Finnish NER corpus12[169] is composed of a selection of pages from journals and newspapers
-published between 1836 and 1918 and digitized by the national library of Finland. The OCR of this
-medium-size corpus was manually corrected by librarians and NE annotations were made manually
-for half of them, semi-automatically for the other (via the manual correction of the output of a
-Stanford NER system trained on the manually corrected subset). Overall, the annotations show a
-good IAA of 0.8 (Cohen’s kappa).
-The HIPE corpus13[55] is a medium-sized, historical news corpus in French, German and English,
-created as part of HIPE-2020. It consists of newspaper articles sampled from Swiss, Luxembourgish
-and American newspaper collections covering a time span of ca. 200 years (1798-2018). OCR
-quality of the corpus corresponds to real-life setting and varies depending on the digitisation time
-and preservation state of original documents. The corpus was annotated following the impresso
-11https://github.com/EuropeanaNewspapers/ner-corpora
-12https://digi.kansalliskirjasto.fi/opendata/submit (Digitalia (2017-2019) package).
-13Version 1.3, https://github.com/impresso/CLEF-HIPE-2020/tree/master/dataNamed Entity Recognition and Classification on Historical Documents: A Survey 17
-guidelines [ 54], which are based on and are retro-compatible with the Quaero guidelines [ 166].
-The annotation tag set comprises 5 coarse-grained and 23 fine-grained entity types, and includes
-entity components as well as nested entities. Wrongly OCRed entity surface forms are manually
-corrected and entities are linked towards Wikidata. NERC and EL annotations reached an average
-IAA across languages of 0.8 (Krippendorf’s alpha).
-The NewsEye dataset14[81] is a large-sized corpus composed of articles extracted from news-
-papers published between mid 19C and mid 20C in French, German, Finnish, and Swedish. Four
-entity types were considered (person, location, organisation and human product) and annotated
-according to guidelines15similar to the impresso ones; entities are linked towards Wikidata and
-articles are further annotated with authors’ stances. The annotation reaches high IAAs exceeding
-0.8 for Swedish and 0.9 for German, French and Swedish (Cohen’s kappa).
-The Quaero Old Press Extended NE corpus16[165] is a very large annotated corpus composed of
-295 pages sampled from French newspapers of December 1890. The OCR quality is rather good, with
-a character and word error rates of 5% and 36.5% respectively. Annotators were asked to transcribe
-wrongly OCRed entity surface forms—similarly to what was done for the HIPE corpus—which
-makes both corpora suitable to check the robustness of NER systems to OCR noise. The annotator
-agreement on this corpus reaches 0.82 (Cohen’s Kappa).
-5.2.2 Literature(s). The second group of corpora relates to literature and is more heterogeneous in
-terms of domains and document types, ranging from literary texts to scholarly publications.
-To begin with, two resources consist of ancient literary texts. First, the Latin NER corpus17[60]
-comprises ancient literary material sampled from three texts representatives of different literary
-genres (prose, letters and elegiac poetry) and spanning over three centuries. The annotation tag set
-covers persons, geographical place names and group names (e.g. ‘Haeduos’, a Gallic tribe). Next,
-the Coptic Scriptorium corpus18is a large-sized collection of literary works written in Coptic, the
-language of Hellenistic era Egypt (3C-5C CE), and belonging to multiple genres (hagiographic
-texts, letters, sermons, martyrdoms and the Bible). Besides lemma and POS tags, this corpus also
-contains (named and non-named) entity annotations, with links towards Wikipedia. In addition to
-persons, places and organisations, the entity types include abstract entities (e.g. ‘humility’), animals,
-events, objects (e.g. ‘bottles’), substances (e.g. ‘water’) and time expressions. Entity annotations
-were produced automatically (resulting in 11k named entities and 6k linked entities), a subset of
-which was manually corrected (2,4k named entities and 1,5k linked entities).
-Then, several corpora were designed to support computational literary analysis. This is the case
-of the BDCamões Collection of Portuguese Literary Documents19[74], a very large annotated
-corpus composed of 208 OCRized texts (4 million words) representative of 14 literary genres and
-covering five centuries of Portuguese literature (16C-21C). Due to the large time span covered, texts
-adhere to different orthographic conventions. Named entity annotations correspond to locations,
-organisations, works, events and miscellaneous entities, and were automatically produced (silver
-annotations). They constitute only one of the many layers of linguistic annotations of this corpus,
-alongside POS tags, syntactic analysis and semantic roles. Next, the LitBank20[12] dataset is a
-medium-sized corpus composed of 100 English literary texts published between mid 19C and
-beginning 20C. Entities were annotated following the ACE guidelines—with the only exception
-14Version 1.0, https://doi.org/10.5281/zenodo.4573313
-15https://zenodo.org/record/4574199
-16http://catalog.elra.info/en-us/repository/browse/ELRA-W0073/
-17https://github.com/alexerdmann/Herodotos-Project-Latin-NER-Tagger-Annotation
-18https://github.com/copticscriptorium/corpora
-19https://portulanclarin.net/
-20https://github.com/dbamman/litbank18 Ehrmann et al.
-of weapons as rarely attested—and include noun phrases as well as nested entities. Finally, the
-Deutsches ROman Corpus (DROC) [ 106] is a set of 90 richly-annotated fragments of German novels
-published between 1650 and 1950. The DROC corpus is enriched with character mentions, character
-co-references, and direct speech occurrences. It features more than 50,000 character mentions, of
-which only 12% (6,013) contain proper names and thus correspond to traditional person entity
-mentions (others correspond to pronouns or appellatives).
-Next, two of the surveyed corpora in this group focus specifically on place names. First, Travel
-writings21[178] is a small corpus of 38 English travelogues printed between 1850 and 1940. Its tag
-set consists of a single type ( Location ), which encompasses geographical, political and functional
-locations, thus corresponding to ACE’s gpe,locandfacentity types altogether. Second, the
-GeoNER corpus22[104] is a very small corpus consisting of three 16C-17C French literary texts by
-Racine, Molière and Marguerite de Valois. Each annotated text is available in its original version, as
-well as with automatic and manual historical spelling normalization. Despite its limited size, this
-corpus can be a valuable resource for researchers investigating the effects of historical normalisation
-on NER.
-Finally, moving from literature to scholarly literature, three corpora should be mentioned. First,
-BIOfid23[2] is a large NE-annotated corpus composed of ca. 1000 articles sampled from German
-books and scholarly journals in the domain of biodiversity and published between 18C and 20C. The
-annotation guidelines used for this corpus build upon those used for the GermEval dataset [ 18], with
-the addition of time expressions and taxonomies ( Taxon ), i.e. systematic classifications of organisms
-by their characteristics (e.g. “northern giant mouse lemur”). Second, HIstory of Medicine CoRpus
-Annotation (HIMERA)24[189] is a small-sized corpus in the domain of medical history, consisting
-of journal articles and medical reports published between 1840 and 2013. This corpus is annotated
-with NEs according to a custom typology comprising, for example, medical conditions, symptoms,
-or biological entities. While all annotations were performed on manually corrected OCR output, the
-annotation of certain types was carried out in a semi-automatic fashion. Globally, the annotation
-reaches good IAAs of 0.8 and 0.86 for exact and relaxed match respectively (F-score). Third, the
-Venetian References corpus25[36] contains about 40,000 annotated bibliographic references from a
-corpus of books and journal articles on the history of Venice (19C-21C century) in Italian, English,
-French, German, Spanish and Latin. Components of references (e.g. author, title, publication date,
-etc.) are annotated according to a custom tag set of 26 tags, and references themselves are classified
-according to the type of work they refer to (e.g. primary vs. secondary sources).
-5.2.3 Other. We found one corpus in the domain of political writings. The De Gasperi corpus26[192]
-consists of the complete collection of public documents by Alcide De Gasperi, Italy’s Prime Minister
-in office from 1945 to 1953 and one of the founding fathers of the European Union. This large
-corpus includes 2,762 documents published between 1901 and 1954 and belonging to a wide variety
-of genres. It was automatically annotated with parts of speech, lemmas, person and place names (by
-means of TextPro [ 146]). This corpus consists of clean texts extracted from the electronic versions
-of previously published volumes.
-21https://github.com/dhfbk/Detection-of-place-names-in-historical-travel-writings
-22https://github.com/PhilippeGambette/GeoNER-corpus
-23https://github.com/FID-Biodiversity/BIOfid
-24http://www.nactem.ac.uk/himera/
-25https://github.com/dhlab-epfl/LinkedBooksReferenceParsing
-26https://github.com/StefanoMenini/De-Gasperi-s-CorpusNamed Entity Recognition and Classification on Historical Documents: A Survey 19
-5.3 Language representations
-As distributional representations, embeddings and language models need to be trained on large
-textual corpora in order to be effective. There exist several large-scale, diachronic collections of
-historical documents, such as the Europeana Newspaper collection [ 133], the Trove Newspaper
-corpus [ 30], the Digi corpus [ 99], and the impresso public corpus [ 52] (to mention but a few), which
-are now used to acquire historical language representations. Given their usefulness in many NLP
-tasks, embeddings and language models are increasingly shared by researchers, thus constituting a
-growing and quickly evolving pool of resources that can be used in historical NER. This section
-inventories existing historical language representations, an overview of which is given in Table 4.
-5.3.1 Static embeddings. As to traditional word embeddings, we could inventory two main re-
-sources. Sprugnoli et al. [ 179] have released a collection of pre-trained word and sub-word English
-embeddings learned from a subset of the Corpus of Historical American English [ 40], considering
-37k texts published between 1860 and 1939 amounting to about 198 million words. These embed-
-dings of 300 dimensions are available according to three types of word representations: embeddings
-based on linear bag-of-words contexts (GloVe [ 143]), on dependency parse-trees (Levy et al. [ 115]),
-and on bag of character n-grams (fastText [ 21]).27Doughman et al. Doughman et al . [46] have
-created Arabic word embeddings from three Lebanese news archives, with materials published
-between 1933 and 2011.28Archive-level as well as decade-level embeddings were trained using
-word2vec with a continuous bag of words model. Given the imperfect OCRed, hyper-parameter
-tuning was used to maximise accuracy on a set of analogy tasks.
-Another set of traditional word embeddings consists of diachronic or dynamic embeddings, i.e.
-static embeddings trained on different time bins of a corpus and thereafter aligned according to
-different strategies (post-hoc alignment after training on different time bins, or incremental training).
-Such resources provide a view of words over time and are usually used in diachronic studies such
-as culturomics and semantic change, but can also be used to feed neural architectures for other
-tasks. Some of the pioneers in releasing such material were Hamilton et al. [ 82], who published a
-collection of diachronic word embeddings29for English, French, German and Chinese, covering
-roughly 19C-20C. They were computed from many different corpora by using word2vec skip-gram
-with negative sampling. Later on, Hengchen et al. [ 83] released a set of diachronic embeddings
-of the same type in English, Dutch, Finnish and Swedish trained on large corpora of 19C-20C
-newspapers.30More recently, Hengchen et al. [ 84] pursued these efforts with the publication of
-diachronic word2vec and fastText models trained on a large corpus of Swedish OCRed newspapers
-(1645-1926) (the Kubhist 2 corpus, 5.5 billion tokens). Thanks to its ability to capture sub-word
-information, their fastText model allows for retrieving OCR misspellings and spelling variations,
-thus being a useful resource for post-OCR correction and historical normalisation.
-5.3.2 Contextualised embeddings. Historical character-level LM embeddings are currently avail-
-able for German, French, and English. For historical German, Schweter et al. [ 172] have trained
-contextualised string embeddings (flair) on articles from two titles from the Europeana newspaper
-collection, the Hamburger Anzeiger (about 741 million tokens, 1888-1945) and the Wiener Zeitung
-(some 801 million tokens, 1703-1875). Resulting embeddings are part of the Flair library.31Next,
-in the context of the HIPE-2020 shared task, fastText word embeddings and flair contextualised
-27For the link to the published embeddings see https://github.com/dhfbk/Histo.
-28Models as well as evaluation details can be found at: https://doi.org/10.5281/zenodo.3538880.
-29https://nlp.stanford.edu/projects/histwords/
-30https://zenodo.org/record/3270648
-31With the ID de-historic-ha-X (HHA) and de-historic-wz-X (WZ) respectively.20 Ehrmann et al.
-Publication Type(s) Model(s) Language(s) Training Corpus
-Hamilton et al. [82] classic word embeddings PPMI, SVD, word2vec de, fr, en, cn Google Books +COHA
-Hengchen et al. [83] classic word embeddings word2vec en, nl, fi, se newspapers and periodicals
-Hengchen et al. [84] char.-based word & word embeddings fastText, word2vec sv Kubhist 2
-Sprugnoli et al. [179] char.-based word & word embeddings dependency-based, fastText, GloVe en CHAE
-Doughman et al. [46] classic word embeddings word2vec ar Lebanese News Archives
-Ehrmann et al. [52, 55] char.-based word & char.-level LM embeddings fastText, flair de,fr,en impresso corpus
-Hosseini et al. [87] all types word2vec, fastText, flair, BERT en Microsoft British Library corpus
-Schweter et al. [172] character-level LM embeddings BERT, ELECTRA de, fr Europeana Newspaper corpus
-Bamman et al. [11] word-level LM embeddings BERT la various Latin corpora
-Table 4. Overview of available word embeddings and LMs trained on historical corpora.
-string embeddings were made available as auxiliary resources for participants.32They were trained
-on newspaper materials in French, German and English, and cover roughly 18C-21C (full details
-in [55] and [ 52]). Similarly, Hosseini et al . [87] published a collection of static (word2vec, fastText)
-and contextualised embeddings (flair) trained on the Microsoft British Library (MBL) corpus. MBL
-is a large-scale corpus composed of 47,685 OCRed books in English (1760-1900) which cover a
-wide range of subject areas including philosophy, history, poetry and literature, for a total of
-approximately 5.1 billion tokens. For each architecture, authors released models trained either on
-the whole corpus or on books published before 1850.
-Word-level LM embeddings trained on historical data are available for Latin, French, German
-and English. Latin BERT is a LM for Latin trained on 640 million tokens spanning 22 centuries.33
-In order to reach a sufficiently large volume of training material, a wide variety of datasets was
-employed including the Perseus Digital Library, the Latin Wikipedia (Vicipaedia), and Latin texts
-of the Internet Archive. Extrinsic evaluation of the model was performed on POS tagging and word
-sense disambiguation, for which Latin BERT demonstrated state-of-the-art results. For historical
-German and French, Schweter [171] published BERT and ELECTRA models trained on two subsets
-of the Europeana newspapers corpus, consisting of 8 and 11 billion tokens for German and French
-respectively. The German models were evaluated on two historical NE datasets, on which the
-ELECTRA models over-performed the BERT ones, leading to an overall improvement on the current
-state-of-the-art results reported by Schweter and Baiter [172] . Finally, for 19C English, BERT-based
-language models trained on the MBL corpus are available in the histLM model collection [ 87]. One
-model was trained on the entire corpus, and additional models were created for different time
-slices to enable the study of linguistic and cultural changes over the 19C, by fine-tuning an existing
-contemporary model (BERT base uncased).
-Conclusion on Resources. Resources for historical NER are not numerous but do exist. A few
-typologies and guidelines adapted for historical OCRed texts were published. More and more
-annotated corpora are being released, but the 17 that we could inventory here are far from the
-121 inventoried in [ 51] for modern NE processing. They are to a large extent built from historical
-newspaper collections, a type of document massively digitised during the last years. If historical
-newspaper contents lend themselves particularly well to NER, this preponderance could also be
-taken as an early warning of the risk of reproducing the news bias already observed for contempo-
-rary NLP [ 149]. Besides, NE-annotated historical corpora show a modest degree of multilingualism,
-and most of them are published under open licenses. As per language representations, historical
-embeddings and language models are not numerous but multiply rapidly.
-32Available at files.ifi.uzh.ch/impresso/clef-hipe-2020/ and on Zenodo platform under DOI 10.5281/zenodo.3706808; Flair
-embeddings were also integrated into the Flair framework: https://github.com/flairNLP/flair. CC BY-NC 4.0 license applies.
-33https://github.com/dbamman/latin-bertNamed Entity Recognition and Classification on Historical Documents: A Survey 21
-6 APPROACHES TO HISTORICAL NER
-This section provides an overview of existing work on NER for historical documents, organised by
-type of approach: rule-based, traditional machine learning and deep learning. The emphasis here
-is more on the implementation and settings of historical NER methods, while strategies to deal
-with specific challenges—regardless of the method—are presented in Section 7. Since research was
-almost exclusively done in the context of individual projects, and since there was no shared gold
-standard up to recently, system performances are often not comparable. We therefore report results
-only when computed on sufficiently large data and explicitly state when results are comparable.
-All works deal with OCRed material unless mentioned otherwise. In absence of obvious thematic
-or technical grouping criteria, they are presented in order of publication (oldest to newest). Table 5
-presents a synthetic view of the reviewed literature.
-6.1 Rule-based approaches
-As for modern NER, first NER works dealing with historical documents were mainly symbolic.
-Rule-based systems do not require training data and are easily interpretable, but need time and
-expertise for designing the rules. Numerous rule-based systems have been developed for modern
-NER, and they usually obtain good results on well-formed texts (see Section 3.4.1).
-Early work performed NER over historical collections using the GATE language technology
-environment [ 38], which supports the manual creation of rules and gazetteers. Those work do
-not include formal evaluations but are worth mentioning as early exploration efforts, e.g. the
-adaptation of rules and gazetteers by Bontcheva et al. [ 22] to recognise Person ,Location ,Occupation
-and Status entity types in 18C English court trials. Among other difficulties, authors mention
-historical occupation names not present in gazetteers, orthographic variations (punctuation, spelling,
-capitalisation), and person name abbreviations.
-Thereafter, most systems relied on custom rule sets and made substantial use of gazetteers, with
-the objective of addressing the domain and language peculiarities of historical documents. Jones et
-al. [95] designed a rule-based system to extract named entities from the Civil War years (1861-1865)
-of the American newspaper the Richmond Times Dispatch (on manually segmented and transcribed
-issues). They focus on 10 entity types, some of them specific to the period and the material at
-hand such as warships, military units and regiments. Their system consists of three main phases:
-gazetteer lookup to extract easily identifiable entities; application of high precision rules to guess
-new names; and learning of frequency-based rules (e.g. how often Washington appears as a person
-rather than a place, and in which context). Best results are obtained for Location andDate, while
-the identification of Person ,Organisation andNewspaper titles is lower. Based on a thorough error
-analysis, authors conclude that shorter but historically relevant gazetteers may be better than long
-ones, and make a plea for the development of comprehensive domain-specific knowledge resources.
-Working on Swedish literary classics from the 19C, Borin et al. [ 23] designed a system made
-of multiple modules: a gazetteer lookup and finite-state grammars module to recognise entities, a
-name similarity module to address lexical variation, and a document centred module to propagate
-labels based on documents’ global evidence. They focused on 8 entity types and evaluated system
-modules’ performances on an incremental basis. On all types together, the best F-measure reaches
-89%, and recall is systematically lower than precision in all evaluation iterations (evaluation setting
-is partial match). The main sources of error are spelling variations, unknown names, and noisy
-word segmentation due to hyphenation in the original document.
-Grover et al. [ 77] focused on two subsets of the Journal of the House of Lords of Great Britain,
-one from the late 17C and the other from early 19C, OCRed with different systems and at different
-times. OCR quality is erratic, and suffers from numerous quotation marks as well as from the22 Ehrmann et al.
-presence of marginalia and of text portions in Latin. An in-house rule-based system, consisting of a
-set of rules applied incrementally with access to a variety of lexica, is applied to recognise person
-and place names. Before NE tagging, the system attempts to identify marginalia words and noisy
-characters in order to ignore them during parsing. The overall performance is evaluated against
-test sets of each period, which comprise significantly more person than location names. Results are
-comparable for person names for both 17C and 19C sets (ca. 75%F-score), but the earliest period has
-significantly worse performance for locations ( 24 .1%and 66 .5%). In most configurations, precision
-is slightly above recall (evaluation setting not specified, most likely exact match). An error analysis
-revealed that character misspellings and segmentation errors (broken NEs) were the main factors
-impacting performances.
-The experiments conducted by Broux et al. [ 28] are part of an initiative aiming at improving
-access to texts from the ancient world. Working with a large collection of documentary texts
-produced between 800 BCE and 800 CE, including all languages and scripts written on any surface
-(mainly papyrological and epigraphical resources), one of the objective is to develop and curate
-onomastic lists and prosopographies of non-royal individuals attested as living during this period.34
-Authors apply a rule-based system benefiting from a huge onomastic gazetteer covering names,
-name variants and morphological variants in several ancient languages and scripts. Rules encode
-various sets of onomastic patterns specific to Greek, Latin and Egyptian (Greek names are ‘simpler’
-than the often multiple Roman names, e.g. Gaius Iulius Caesar ) and specifically designed to capture
-genealogical information. This system is used to speed up manual NE annotation of texts, which
-in turn is used for network analysis in order to assist the creation of prosopographies. No formal
-evaluation is provided.
-Fast-forwarding to contemporary times, Kettunen et al. [ 100] experimented with NER on a
-collection of Finnish historical newspapers from late 19C - early 20C. Authors insist on the overall
-poor quality of the OCR (word level correctness around 70%−75%), as well as on the fact that
-they use an existing rule-based system designed for modern Finnish with no adaptation. Not
-surprisingly, this combination leads to rather low results with F-scores ranging from 30%to45%
-for the 8 targeted entity types (evaluation setting is exact match). The main sources of errors are
-bad OCR and multi-word entities.
-A recent work by Platas et al. [ 150] focuses on a set of manually transcribed Medieval Spanish texts
-(12C-15C) covering various genres such as legal documents, epic poetry, narrative, or drama. Based
-on the needs of literary scholars and historians, the authors defined a custom entity typology of 8
-main types (plus sub-types). It covers traditional but also more specific types for the identification
-of name parts, especially relevant for Medieval Spanish person names featuring many attributes
-and complex syntactic structures ( Don Alfonso por la gracia de Dios rey de Castiella de Toledo de
-Leon de Gallizia de Seuilla de Cordoua de Murcia e de Jaen ). The system is composed of several
-modules dedicated to recognising names using rules and/or gazetteers, increasing the coverage
-using variant generation and matching, and recognising person attributes using dependency parsing.
-Evaluated on a manually annotated corpus representative of the time periods and genres of the
-collection, the system reached satisfactory results with an overall F-score of 77%, ranging from
-74%to87%depending on the entity type (evaluation setting is exact match). As usual, recall is
-lower than precision, but differences are not high. Although these numbers are lower than what
-neural-based systems can achieve, this demonstrates the capacities and suitability of a carefully
-designed rule-based system.
-34Onomastic relates to the study of the history and origin of proper names (Oxford English dictionary), and prosopography
-relates to the collection and study of information about a person.Named Entity Recognition and Classification on Historical Documents: A Survey 23
-Finally, it is also worth mentioning a series of work on the geoparsing of historical and literary
-texts. With the aim of analysing the interplay between geographical and fictional landscapes,
-Moncla et al . [128] experimented with a rule-based system relying on extensive gazetteers to
-recognise names of streets, houses, bridges, etc. in French Parisian novels from the 19C. With
-spatial entities featuring a high degree of regularity, the system reached very good results on a
-relatively small test set (evaluation settings are not entirely clear). Adapting the existing Edinburgh
-Geoparser system (derived from Grover et al . [77] above) for historical texts, Alex et al. [ 5] carried
-out experiments to recognise place names in different types of 19C British historical documents.
-Besides the impact of OCR errors, main observations are that it is essential to perform place and
-person name recognition in tandem in order to better handle homonyms—even when dealing with
-place names only—, and that gazetteers need substantial adaptation, with careful switching on
-and off of standard vs domain-specific lexica. This system was also applied on a set of historical
-Edinburgh-specific documents, this time targeting fine-grained location names and considering
-three types of material: OCRed documents from 19C British novels, manually crowd-corrected
-OCRed texts from the Project Gutenberg collection, and contemporary (born-digital) texts from
-Scottish authors [ 7]. Not surprisingly, place name recognition performs best on contemporary texts
-(but remains low with an F-score of 75%), worst on historical OCRed text (F-score 68%), and roughly
-in-between on crowd-corrected OCRed documents (F-score 72%). Precision scores are similar across
-the three collections, but recall scores vary considerably. Much research has been done on the
-geoparsing of cultural heritage material but is not further surveyed here.
-Conclusion on rule-based approaches . Symbolic approaches were applied on a large variety
-of document types, domains and time periods (see Table 5 for an overview of characteristics).
-In general, rule-based systems are modular and almost systematically include gazetteer lookup,
-rule incremental application, and variant matching. They have difficulties dealing with noisy and
-historical input, for which they require normalisation rules and additional linguistic knowledge.
-The number of work we could inventory, from the beginning of the 2000s until today, confirms
-the long-standing need for NER on historical documents as well as the suitability of symbolic
-approaches that can be better dealt with by non experts. Research nevertheless moved away from
-such systems in favour of machine learning ones.
-6.2 Traditional Machine Learning Approaches
-Machine learning algorithms inductively learn statistical models from annotated data on the basis of
-manually selected features (see Section 3.4.2). Heavily researched and applied in the 2000s, machine
-learning-based approaches contributed strong baselines for mainstream NER, and were rapidly
-adopted for NER on historical documents. In this section we review the usage of such traditional,
-pre-neural machine learning approaches on historical material, first considering works which apply
-already existing models, second which train new ones.
-6.2.1 Applying existing models. Early achievements adopted the ‘off-the-shelf’ strategy with the
-application of pre-trained NER systems or web services to various historical documents, mainly
-with the objectives of assessing baselines and/or comparing system performances. This is the
-case of Rodriquez et al. [ 163], who compared the performances of four NER systems (Stanford
-CRF classifier, OpenNLP, AlchemyAPI, and OpenCalais) on two English datasets related to WWII:
-individual Holocaust survivor testimonies from the Wiener Library of London and letters of soldiers
-from King’s College archive. Evaluated on a small dataset, the recognition of Person ,Location and
-Organization reached an F-score between 47%and 54%for the testimonies (Stanford CRF being the
-most accurate), and between 32%and 36%for the letters (OpenCalais performing best). Surprisingly,
-running the same evaluation on manually corrected OCR did not improve results significantly.24 Ehrmann et al.
-Major sources of errors were different ways of naming and metonymy phenomena (e.g. warships
-named after people), and lack of background knowledge, especially for organisations.
-Along the same line, Ehrmann et al. [ 50] conducted experiments on French historical newspapers
-on a diachronic basis (covering 200 years) for the types Person andLocation , with the objective of
-investigating whether NER performance degrades when going back in time. Their study includes
-four systems representative of major approaches for NER: a rule-based system, a supervised machine
-learning one (MaxEnt classifier), and two proprietary web services offering NER functionalities
-(AlchemyAPI and DandelionAPI). They showed that, compared to a baseline on contemporary news,
-all systems feature degraded performances, both in absolute terms and over time (maximum of 67 .6%
-F-score for person names for the best system, with exact match). As for time-based observation,
-precision is quite irregular, with several ups and downs for all systems for both entity types, but
-recall shows less variability and a slight but regular increase for Person , suggesting that person
-names are less stable than location names and therefore better recognised when more recent.
-Focusing on the impact of historical language normalisation (in this respect see also Section
-7.2), Kogkitsidou et al. [ 104] also used and benchmarked several systems (rule-based and machine
-learning) for the recognition of Location names in French literary texts from the 16C and 17C.
-When applied without any adaptation, systems features very diverse performances, from very low
-(36%) to reasonable ( 70%) F-scores, with rule-based ones being better at precision, and machine
-learning ones at recall.
-Ritze et al. [ 160] worked on historical records of the English High Court of Admiralty of the
-17C and used the Stanford CRF classifier with its default English model to recognise Person and
-Location types (others were considered but not evaluated). Given the very specific domain of this
-corpus, obtained results were reasonable, with a precision in the 77%for both types (recall was not
-reported).
-Finally, some adopt the approach of ensembling systems, i.e. of considering NE predictions not
-from one but several recognisers, according to various voting strategies. Packer et al. [ 138] applied
-three algorithms (dictionary-based, regular expressions-based, and HMM-based) in isolation and in
-combination for the recognition of person names in various types of English OCRed documents.
-They observed increased performances (particularly a better P/R balance) with a majority vote
-ensembling. Won et al. [ 198] worked on British personal archives from 16C and 17C and applied
-five different systems to recognise place names. They too observed that the combination of multiple
-systems through a majority vote (with a minimum of two to a maximum of three votes) was able to
-consistently outperform the individual NER systems.
-Mere application of existing systems, these work illustrate the inadequacy of already trained
-NER models for historical texts. Performances (and settings) of these baseline studies are extremely
-diverse, but the following constants are observed: recall is always the most affected, and the Location
-type is usually the most robust.
-6.2.2 Training models. Other work trained NER systems anew on custom material. Early attempts
-include the experiments of Nissim et al. [ 135] on Location entity type in manually transcribed
-Scottish parish registers of the late 18C and early 19C. They trained a maximum entropy tagger
-with its in-built standard features on a dataset of ca. 6000 location mentions and obtained very
-satisfying performance ( 94 .2%F-score), which they explained by the custom training data and the
-binary classification task (location vs non-location).
-Subsequently, the most frequently used system is the Stanford CRF classifier35[63], particularly
-on historical newspapers. Working with the press collection of the National Library of Australia,
-Kim et al. [ 103] evaluated two Stanford CRF models, the default English one trained on CoNLL-03
-35https://nlp.stanford.edu/software/CRF-NER.htmlNamed Entity Recognition and Classification on Historical Documents: A Survey 25
-Publication Domain Document type Time period Language(s) System Comp.
-Rule-based
-Bontcheva et al. [22] legal court trials 18C en-GB rule-based
-Jones et al. [95] news newspapers mid 19C en-US rule-based
-Borin et al. [23] literature literary classics 19C sv rule-based
-Grover et al. [77] state parliamentary proc. 17C & 19C en-GB rule-based
-Broux and Depauw [28] state papyri 4C-1C bce egy, el, la lookup
-Kettunen et al. [100] news newspapers 19C-20C fi rule-based
-Alex et al. [5] state/literature parl. proc./classics var en-scotland lookup
-Alex et al. [7] literature novels 19C en-scotland lookup
-Moncla et al. [128] literature novels 19C fr lookup
-Platas et al. [150] literature poetry, drama 12C-15C es rule-based
-Traditional machine learning
-Nissim et al. [135] admin parish registers 18C-19C en-scotland MaxEnt
-Packer et al. [138] mix various - en ensemble
-Rodriquez et al. [163] egodocs letters & testimonies WWII en-GB several
-Galibert et al. [67] news newspapers 19C fr several
-Dinarelli et al. [44] news newspapers 19C fr CRF+PCFG
-Ritze et al. [160] state admiralty court rec. 17C en-GB CRF
-Neudecker et al. [134] news newspapers 19C-20C de, fr, nl CRF
-Passaro et al. [141] state war bulletins 20C it CRF
-Kim et al. [103] news newspapers - en CRF
-Ehrmann et al. [50] news newspapers 19C-20C fr several
-Aguilar et al. [1] news medieval charters 10C-13C la CRF
-Erdmann et al. [60] literature classical texts 1C bce-2C la CRF
-Ruokolainen et al. [169] news newspapers 19C-20C fi CRF+gaz
-Won et al. [198] egodocs letters 17-18C en-GB ensemble
-El Vaigh et al. [58] news newspapers ( hipe) 19C-20C de, en, fr CRF
-Kogkitsidou et al. [104] literature theatre and memoirs 16C-17C French several
-Deep Learning
-Riedl et al. [158] news newspapers 19C-20C de BiLSTM-CRF ♢
-Rodrigues A. et al. [162] bibliometry journals & monographs 19C-20C multi BiLSTM-CRF
-Sprugnoli [178] literature travel writing 19C-20C en-US BiLSTM-CRF
-Ahmed et al. [2] biodiversity scholarly pub. 19C-20C de BiLSTM-CRF
-Kew et al. [101] literature alpine texts 19C-20C multi BiLSTM-CRF
-Schweter et al. [172] news newspapers 19C-20C de BiLSTM-CRF ♢
-Labusch et al. [108] news newspapers 19C-20C de BERT ♢
-Dekhili and Sadat [41] news newspapers ( hipe) 19C-20C fr BiLSTM-CRF ♦
-Ortiz S. et al. [137] news newspapers ( hipe) 19C-20C fr, de BiLSTM-CRF ♦
-Kristanti et al. [105] news newspapers ( hipe) 19C-20C en, fr BiLSTM-CRF ♦
-Provatorova et al. [152] news newspapers ( hipe) 19C-20C de, en, fr BiLSTM-CRF ♦
-Todorov et al. [191] news newspapers ( hipe) 19C-20C de, en, fr BiLSTM-CRF ♦
-Schweter et al. [173] news newspapers ( hipe) 19C-20C de BiLSTM-CRF ♦
-Labusch et al. [107] news newspapers ( hipe) 19C-20C de, en, fr BERT ♦
-Ghannay et al. [70] news newspapers ( hipe) 19C-20C fr ♦
-Boros et al. [25] news newspapers ( hipe) 19C-20C de, en, fr BERT ♦
-Swaileh et al. [184] economy financial yearbooks 20C de, fr BiLSTM-CRF
-Yu et al. [203] history state official books 1 bce-17C zh BERT
-Hubková et al. [91] news newspapers 19C-20C cz BiLSTM
-Table 5. Historical NER literature overview. Papers are grouped by family of approaches and ordered by
-publication year. ‘ Comp. ’ stands for comparable and denotes works whose results are obtained on same test
-sets.26 Ehrmann et al.
-English data, and a custom one trained on 600 articles of the Trove collection (the time period of the
-sample is not specified). Interestingly, the model trained on in-domain data did not outperform the
-default one, and both yielded F-scores around 75%forPerson andLocation , with a drop below 50%for
-Organisation . Neudecker et al. [ 134] focused on newspaper material in French, German and Dutch
-from the Europeana collection [ 132], on which they trained a Stanford CRF model with additional
-gazetteers. The 4-fold cross-evaluation yielded F-scores in the range of 70-80% for Dutch and French,
-while no results were reported for German. For both languages, recall was significantly lower than
-precision. Working on Finnish historical newspapers, Ruokolainen et al. [ 169] considered Person
-andLocation and trained the Stanford CRF classifier on manually corrected OCRed material, with
-large gazetteers covering inflected forms. The model gave satisfying performances with F-scores of
-87%(location) and 80%(person) on a test set taken from the same manually corrected data, and of
-78%and 71%on non-corrected OCR texts (with recall being lower than precision). This time on
-French, and taking advantage of the Quaero Old Press corpus, Galibert et al. [ 67] organised a small
-evaluation campaign where three anonymous systems participated. Stochastic systems performed
-best (especially on noisy entities), with an F-score of 65 .2%across all types (person, location and
-organisation). Also working on French newspapers in the context of HIPE-2020, Elvaigh et al. [ 58]
-(slightly) fine-tuned the CRF baseline provided by the organisers and reached 66%on all types
-(exact match), two points more than the baseline.
-Going back in time, Aguilar et al. [ 1] experimented NER on manually transcribed Latin medieval
-charters from the 10C to 13C. Focusing on person and place names, they used dedicated pre-
-processing and trained a CRF classifier using the Wapiti toolkit.36Results are remarkable, on
-average in the 90%for both types, certainly due to the regularity of the documents in terms of
-names, naming conventions, context and overall structure.
-Finally, Passaro et al. [ 141] attempted to extract entities from WWI and WWII Italian official
-war bulletins. They focused on the traditional entity types, plus Military organisations ,Ships and
-Airplanes . The Stanford system was trained (without gazetteers) on semi-automatically annotated
-data from the two periods as well as on contemporary Italian news, and various experiments mixing
-in- vs. out-of-time data were carried out. Results showed that performances are highest when
-the model is trained on data close in time, that entities of type Location are systematically better
-recognised, and that custom types (ships, military organisations, etc.) are poorly recognised.
-Conclusion on traditional machine learning approaches. Overall, the availability of machine
-learning-based NER systems that could either be applied as such or trained on new material greatly
-fostered a second wave of experiments on historical documents. Settings are quite diverse, and
-so are the performances, but F-scores are usually in the order of 60−70%, which is significantly
-lower than those usually obtained on contemporary material (frequently in the 90%). The Stanford
-CRF classifier is by far the most commonly used, as well as CRF in general. Not surprisingly,
-performances are higher when systems are trained on in-domain material.
-6.3 Deep Learning Approaches
-Latest developments in historical NER are dominated by deep learning techniques which have
-recently shown state-of-the-art results for modern NER. Deep learning-based sequence labelling
-approaches rely on word and character distributed representations and learn sentence or sequence
-features during end-to-end training. Most models are based on BiLSTM architectures or self-
-attention networks, and use a CRF layer as tag decoder to capture dependencies between labels (see
-Section 3.4.3). Building on these results, much work attempt to apply and/or adapt deep learning
-approaches to historical documents, under different settings and following different strategies.
-36https://wapiti.limsi.fr/Named Entity Recognition and Classification on Historical Documents: A Survey 27
-6.3.1 Preliminary comments. Let us begin with some observations on the main lines of research.
-In a feature learning context the crucial point is, by definition, the capacity of the model to learn
-or reuse appropriate knowledge for the task at hand. Given a situation of time and domain shifts
-and of resource scarcity, what is at stake for neural-based historical NER approaches is to capture
-historical language idiosyncrasies (including OCR noise) and to adequately leverage previously
-learned knowledge — a process made increasingly possible with the usage of pre-trained language
-models in a transfer learning context. Transfer learning (TL) refers to a set of methods which
-aims at leveraging knowledge from a source setting and adapting it to a target setting [ 140]. TL is
-not new in NLP but was recently given considerable momentum, in particular sequential transfer
-learning where the source task (e.g. language modeling) differs from the target task (e.g. NER).
-In this supervised TL setting, a widely used process is to first learn representations on a large
-unlabelled corpus (source), before adapting them to a specific task using labelled data (target). The
-previously learned model can be adapted to the target task in different ways, the most frequent
-being weight adaptation, where pre-trained weights are either kept unchanged (‘frozen’) and used
-as features in the downstream model (feature extraction), or fine-tuned to the target task and used
-as initialisation in the downstream model (fine-tuning) [168].
-To date, most DL approaches to historical NER have primarily focused on experimenting with
-a) different input representations, that is to say embeddings of different granularity (character,
-sub-word, word), learned at the type or token level (static vs. contextualised) and derived from
-domain data or not (in vs. out-of-domain), and b) different transfer learning strategies. Those aspects
-are often intermingled in various experiments reported in the literature, which does not easily lend
-itself to a clear-cut narrative outline. The discussion which follows is organised according to the
-demarcation line ‘words vs. words-in-context’, complemented with observations on TL settings and
-types of networks. However imperfect this line is, it reflects the recent evolution of incorporating
-more context and of testing all-round language models in historical settings. As a complement,
-and in order to frame further the discussion, we identified a set of key research questions from the
-types of experiments reported in publications, summarised in Table 6.
-6.3.2 Approaches based on static embeddings. First attempts are based on state-of-the-art BiLSTM-
-CRF and investigate the transferability of various types of pre-trained static embeddings to historical
-material. They all use traditional CRFs as baseline.
-Focusing on location names in 19-20C English travelogues,37Sprugnoli [ 178] compares two
-classifiers, Stanford CRF and BiLSTM-CRF, and experiment with different word embeddings: GloVe
-embeddings, based on linear bag-of-words contexts and trained on Common Crawl data [ 143],
-Levy and Goldberg embeddings, produced from the English Wikipedia with a dependency-based
-approach [ 115], and fastText embeddings, also trained on the English Wikipedia but using sub-word
-information [ 21]. Additionally to these pre-trained vectors, Sprugnoli trains each embedding type
-afresh on historical data (a subset of the Corpus of Historical American English), ending up with
-3×2 input options for the neural model. Both classifiers are trained on a relatively small labelled
-corpus. Results show that the neural approach performs systematically and remarkably better than
-CRF, with a difference ranging from 11 to 14 F-score percentage points, depending on the word
-vectors used (best F-score is 87.4 %). If in-domain supervised training improves the F-score of the
-Stanford CRF module, it is worth noting that the gain is mainly due to recall, the precision of the
-English default model remaining higher. In this regard, the neural approach shows a better P/R
-balance across all settings. With respect to embeddings, linear bag-of-words contexts (GloVe) prove
-to be more appropriate (at least in this context), with its historical embeddings yielding the highest
-scores across all metrics (fastText following immediately after). A detailed examination of results
-37Corpus presented in Section 5.2.2.28 Ehrmann et al.
-Research questions Experiments Publication
-Input representation
-Which type of embedding is best?
-Test different static embedding algorithms [178]
-Test different static embedding granularity [162]
-Use modern static embeddings (word2vec, fastText) [91]
-Use modern char-level LM embeddings (Flair) [184]
-Use modern word-level LM embeddings (BERT, ELMo) [70, 152, 203]
-Uses stack of modern embeddings [105, 137, 162]
-Transfer learning
-How well modern embeddings can transfer to historical texts?
-What is the impact of in-domain embeddings?
-Is more task-specific labelled data more helpful than big or in-domain LMs?
-Test modern vs. historical static embeddings [158]
-Test modern vs. historical char-level LM embeddings [41, 101, 137, 172, 173]
-Test modern vs. historical word-level LM embeddings [2, 108, 172]
-Test stack of embeddings [2, 25, 108, 172, 173, 191]
-Test feature extraction (frozen) vs. fine-tuning [91, 152, 162]
-Test different training corpus sizes [2, 105, 158]
-Test cross-corpus model application [25, 105, 108, 158, 191]
-Test cross-corpus model training [158]
-Neural architecture
-How neural approaches compare to traditional CRFs?
-What is the best neural architecture with which decoder?
-Compare BiLSTM and traditional CRF [137, 158, 162, 178]
-Compare CRF decoder vs. softmax decoder [162]
-Compare BiLSTM and LSTM [91]
-Test single vs. multitask learning [162, 191]
-Compare transformers and BiLSTM [25]
-Table 6. Synthetic view of DL experiments mapped with research questions.
-reveals an uneven impact of in-domain embeddings, leading either to higher precision but lower
-recall (Levy and GloVe), or higher recall but lower precision (fastText and GloVe). Overall, this
-work shows the positive impact of in-domain training data: the BiLSTM-CRF approach, combined
-with in-domain training set and in-domain historical embeddings, systematically outperforms the
-linear CRF classifier.
-In the context of reference mining in the arts and humanities, Rodriguez et al. [ 162] also inves-
-tigate the benefit of BiLSTM over traditional CRFs, and of multiple input representations. Their
-experiments focus on three architectural components: input layer (word and character-level word
-embeddings), prediction layers (Softmax and CRF), and learning setting (multi-task and single-task).
-Authors consider a domain-specific tagset of 27 entity types covering reference components (e.g.
-author, title, archive, publisher) and work with 19-21C scholarly books and journals featuring a
-wide variety of referencing styles and sources.38While character-level word embeddings, likely to
-38Corpus presented in Section 5.2.2Named Entity Recognition and Classification on Historical Documents: A Survey 29
-help with OCR noise and rare words, are learned either via CNN or BiLSTM, word embeddings
-are based on word2vec and are tested under various settings: present or not, pre-trained on the in-
-domain raw corpus or randomly initialised, and frozen or fined-tuned on the labelled corpus during
-training. Among those settings, the one including in-domain word embeddings further fine-tuned
-during training and CRF prediction layer yields the best results ( 89 .7%F-score). Character-level
-embeddings provide a minor yet positive contribution, and are better learned via BiLSTM than
-with CNN. The BiLSTM architecture outperforms the CRF baseline by a large margin (+ 7%), except
-for very infrequent tags. Overall, this work confirms the importance of word information (rather
-in-domain, though here results with generic embeddings were not reported) and the remarkable
-capacities of a BiLSTM network to learn features, better decoded by a CRF classifier than a softmax
-function.
-Working with Czech historical newspapers,39Hubková et al. [ 91] target the recognition of
-five generic entity types. Authors experiment with two neural architectures, LSTM and BiLSTM,
-followed by a softmax layer. Both are trained on a relatively small labelled corpus (4k entities) and
-fed with modern fastText embeddings (as released by the fastText library) under three scenarios:
-randomly initialised, frozen, and fine-tuned. Character-level word embeddings are not used. Results
-show that the BiLSTM model based on pre-trained embeddings with no further fine-tuning performs
-best ( 73%F-score). Authors do not comment on the performance degradation resulting from fine-
-tuning, but one reason might be the small size of the training data.
-Rather than aiming at calibrating a system to a specific historical setting, Riedl et al. [ 158]
-adopt a more generic stance and investigate the possibility of building a German NER system
-that performs at the state of the art for both contemporary and historical texts. The underlying
-question—whether one type of model can be optimised to perform well across settings— naturally
-resonates with the needs of cultural heritage institution practitioners (see also Schweter et al. [ 172]
-and Labush et al. [ 108] hereafter). Experimental settings consist of: two sets of German labelled
-corpora, with large contemporary datasets (CoNNL-03 and GermEval) and small historical ones
-(from the Friedrich Temann and Austrian National library); two types of classifiers, CRFs (Stanford
-and GermaNER) and BiLSTM-CRF; finally, for the neural system, usage of fastText embeddings
-derived from generic (Wikipedia) and in-domain (Europeana corpus) data. On this base, authors
-perform three experiments. The first investigates the performances of the two types of systems on
-the contemporary datasets. On both GermEval and CoNNL, the BiLSTM-CRF models outperform the
-traditional CRF ones, with Wikipedia-based embeddings yielding better results than the Europeana-
-based ones. It is noteworthy that the GermaNER CRF model performs better than the LSTM of
-Lample et al. [ 110] on CoNLL-03, but suffers from low recall compared to BiLSTM. The second
-experiment focuses on all-corpora crossing, with each system being trained and evaluated on all
-possible combinations of contemporary and historical corpora pairs. With no surprise, best results
-are obtained when models are trained and evaluated on the same material. Interestingly, CRFs
-perform better than BiLSTM in the historical setting (i.e. train and test sets from historical corpora)
-by quite a margin, suggesting that although not optimised for historical texts, CRFs are more robust
-than BiLSTM when faced with small training datasets. The type of embeddings (Wikipedia vs.
-Europeana) plays a minor role in the BiLSTM performance in the historical setting. Ultimately, the
-third experiment explores how to overcome this neural net dependence on large data with domain
-adaptation transfer learning: the model is trained on a contemporary corpus until convergence and
-then further trained on a historical one for a few more epochs. Results show consistent benefits
-for BiLSTM on historical datasets (ca. +4 F-score percentage points). In general, main difficulties
-relate to OCR mistakes and wrongly hyphenated words due to line breaks, and to the Organisation
-39Corpus presented in Section 5.2.130 Ehrmann et al.
-type. Overall, this work shows that BiLSTM and CRF achieve similar performances in a small-data
-historical setting, but that BiLSTM-CRF outperforms CRF when supplied with enough data or in a
-transfer learning setting.
-This first set of work confirms the suitability of the state-of-the-art BiLSTM-CRF approach for
-historical documents, with the major advantage of not requiring feature engineering. Provided
-that there is enough in-domain training data, this architecture obtains better performances than
-traditional CRFs (the latter performing on par or better otherwise). In-domain pre-training of static
-word embeddings seems to contribute positively, although to various degrees depending on the
-experimental settings and embedding types. Sub-word information (either character embeddings
-or character-based word embeddings) also appears to have positive effect.
-6.3.3 Approaches based on character-level LM embeddings. Approaches described above rely on
-static, token-level word representations which fail to capture context information. This drawback
-can be overcome by context-dependent representations derived from the task of modelling language,
-either as distribution over characters, such as the Flair contextual string embeddings [ 3], or over
-words, such as BERT [ 43] and ELMo [ 144] (see Section 3.3.3). Such representations have boosted
-performances of modern NER and are also used in the context of historical texts. This section
-considers work based on character-based contextualised embeddings (flair).
-In the context of the CLEF-HIPE-2020 shared task [ 53], Dekhili et al. [ 41] proposed different
-variations of a BiLSTM-CRF network, with and without the in-domain HIPE flair embeddings
-and/or an attention layer. The gains of adding one or the other or both are not easy to interpret,
-with uneven performances of the model variants across NE types. Their overall F-scores range from
-62%to65%under the strict evaluation regime. For some entity types the CRF baseline is better than
-the neural models, and the benefit of in-domain embeddings is overall more evident than the one
-of the attention layer (which proved more useful in handling metonymic entities).
-Kew et al . [101] address the recognition of toponyms in an alpine heritage corpus consisting of
-over 150 years of mountaineering articles in five languages (mainly from the Swiss and British
-Alpine Clubs). Focusing on fine-grained entity types (city, mountain, glacier, valley, lake, and cabin),
-the authors compare three approaches. The first is a traditional gazetteer-based approach completed
-with a few heuristics which achieves high precision across types ( 88%P,73%F-score), and even very
-high precision ( >95%) for infrequent categories with regular patterns. Suitable for reliable location-
-based search but suffering from low recall, this approach is then compared with a BiLSTM-CRF
-architecture. The neural system is fed with stacked embeddings composed of in-domain contextual
-string embeddings pre-trained on the alpine corpus concatenated with general-purpose fastText
-word embeddings pre-trained on web data, and trained on a silver training set containing 28k
-annotations obtained via the application of the gazetteer-based approach. The model leads to
-an increase of recall for the most frequent categories, without degrading precision scores ( 76%
-F-score). This shows the generalisation capacity of the neural approach in combination with context-
-sensitive string embeddings and given sufficient training data. Finally, authors experiment with
-crowd-corrected annotations and observe that already a small number of corrections on the silver
-data has a positive impact (+3 F-score percentage point).
-Swaileh et al . [184] target even more specific entity types in French and German financial
-yearbooks from the first half of 20C. They apply a BiLSTM-CRF network trained on custom data
-and fed with modern flair embeddings. Results are very good (between 85%to95%F-score depending
-on the book sections), with the CRF baseline and the BiLSTM model performing on par for French
-books, and BiLSTM being better than CRF for the German one, which has a lower OCR quality.
-Overall, these performances can be explained by the regularity of the structure and language as
-well as the quality of the considered material, resulting in stable contexts and non-noisy entities.Named Entity Recognition and Classification on Historical Documents: A Survey 31
-6.3.4 Approaches based on word-level LM embeddings. The release of pre-trained contextualised
-language model-based word embeddings such as BERT (based on transformers) and ELMo (based on
-LSTM) pushed further the upper bound of modern NER performances. They show promising results
-either in replacement or in combination with other embedding types, and offer the possibility of
-being further fine-tuned [ 116]. If they are becoming a new paradigm of modern NER, the same
-seems to be true for historical NER.
-Using pre-trained modern embeddings. We first consider work based on pre-trained modern
-LM-based word embeddings (BERT or ELMo) without extensive comparison experiments. They
-make use of BiLSTM or transformer architectures.
-Working on the “Chinese Twenty-Four Histories”, a set of Chinese official history books covering
-a period from 3000 BCE to 17C, Yu et al. [ 203] face the problems of the complexity of classical
-Chinese and of the absence of appropriate training data in their attempt to recognise Person and
-Location . Their BiLSTM-CRF model is trained on a NE-annotated modern Chinese corpus and
-makes use of modern Chinese BERT embeddings in a feature extraction setting (frozen). Evaluated
-on a (small) dataset representative of the time span of the target corpus, the model achieves
-relatively good performances (from 72%to82%F-score depending on the book), with a pretty good
-P/R balance, better results for Location than for Person , and on recent books. Given the complete
-‘modern’ setting of embeddings and training labelled data, those results shows the benefit of large
-LM-based embeddings—keeping in mind the small size of the test set and perhaps the regularity of
-entity occurrences in the material, not detailed in the paper.
-Also based on the bare usage of state-of-the-art LM-based representations is a set of work from
-the HIPE-2020 evaluation campaign. These work tackle the recognition of five entity types in about
-200 years of historical newspapers in French, English, and German.40The task included various NER
-settings, however only the coarse literal NE recognition is considered here. Ortiz Suárez et al . [137]
-focused on French and German. They first pre-process the newspaper line-based format (or column
-segments) into sentence-split segments before training a BiLSTM-CRF model using a combination
-of modern static fastText and contextualised ELMo embeddings as input representations. They
-favoured ELMo over BERT because of its capacity to handle long sequences and its dynamic
-vocabulary thanks to its CNN character embedding layer. In-domain fastText embeddings provided
-by the organisers were tested but performed lower. Their models ranked third on both languages
-during the shared task, with strict F-score of 79%and 65%for French and German respectively.
-The considerably lower performance of their improved CRF baseline illustrates the advantage of
-contextual embeddings-based neural models. Ablation experiments on sentence splitting showed
-an improvement of 3.5 F-score percentage points on French data (except for Location ) confirming
-the importance of proper context for NER neural tagging.
-Running for French and English, Kristanti et al. [ 105] also make use of a BiLSTM-CRF relying
-on modern fastText and ELMo emddings. In the absence of training set for English, authors use
-the CoNLL-2012 corpus, while for French the training data is further augmented with another NE-
-annotated journalistic corpus from 1990, which proved to have positive impact. They scored at 63%
-and 52%in terms of strict F-score for French and English respectively. Compared to the French results
-of Ortiz Suàez et al., Kristanti et al. use the same French embeddings but a different implementation
-framework and different hyper-parameters, and does not apply sentence segmentation.
-Finally, still within the HIPE-2020 context, two teams tested pre-trained LM embeddings with
-transformer-based architectures. Provatorova et al . [152] proposed an approach based on the fine-
-tuning of BERT models using Huggingface’s transformer framework for the three shared task’s
-languages, using the cased multilingual BERT base model for French and German and the cased
-40Corpus presented in Section 5.2.1.32 Ehrmann et al.
-monolingual BERT base model for English. They used the CoNLL-03 data for training their English
-model, the HIPE data for the others, and additionally set up a majority vote ensemble of 5 fine-tuned
-model instances per language in order to improve the robustness of the approach. Their models
-achieved F-scores of 68%,52% and 47% for French, German and English respectively. Ghannay
-et al. [70] used CamemBERT, a multi-layer bidirectional transformer similar to ROBERTa [ 119,124]
-initialised with a pre-trained modern French CamemBERT and completed with a CRF tag decoder.
-This model obtained the second-best results for French with 81%strict F-score.
-Even when learned from modern data, pre-trained LM-based word embeddings encode rich prior
-knowledge that effectively support neural models trained on (usually) small historical training sets.
-As for HIPE-related systems, it should be noted that word-level LM embeddings systematically
-lead to slightly higher recall than precision, demonstrating their powerful generalisation capacities,
-even on noisy texts.
-Using modern and historical pre-trained embeddings. As for static embeddings, it is logical to expect
-higher performances from LM-embeddings when pre-trained on historical data, in combination
-with modern ones or not. The set of work reviewed here explores this perspective.
-Ahmed et al . [2] work on the recognition of universal and domain-specific entities in German
-historical biodiversity literature.41They experiment with two BiLSTM-CRF implementations (their
-own and Flair framework) which both use modern token-level German word embeddings and
-are trained on the BIOfid corpus. Experiments consist in adding richer representations (modern
-Flair embeddings, additionally completed by newly trained ELMo embeddings or BERT base
-multilingual cased embeddings) or adding more task-specific training data (GermEval, CoNLL-03
-and BIOfid). Models perform more or less equally, and authors explained the low gain of in-domain
-ELMo embdedings by the small size of the training data (100k sentences). Higher gains come with
-larger labelled data, however the absence of ablation tests hinders the complete understanding
-of the contribution of the historical part of this labelled data, and the use of two implementation
-frameworks does not warrant full results comparability.
-Both Schweter et al. [ 172] and Labusch et al. [ 108] build on the work of Riedl et al. [ 158] and
-try to improve NER performances on the same historical German evaluation datasets, thereby
-constituting (with HIPE-2020) one of the few sets of comparable experiments. Schweter et al. seek
-to offset the lack of training data by using only unlabelled data via pre-trained embeddings and
-language models. They use the Flair framework to train and combine (“stack”) their language
-models, and to train a BiLSTM-CRF model. Their first experiment consists in testing various static
-word representations, with: character embeddings learned during training, fastText embeddings
-pre-trained on Wikipedia or Common Crawl (with no sub-word information), and the combination
-of all of these. While Riedl et al. experimented with similar settings (character embeddings and
-pre-trained modern and historical fastText embeddings), it appears that combining Wikipedia and
-Common Crawl embeddings leads to better performances, even higher than the transfer learning
-setting of Riedl et al. using more labelled data. As a second experiment, Schweter et al. use pre-
-trained LM embeddings: flair embeddings newly trained on two historical corpora having temporal
-overlaps with the test data, and two modern pre-trained BERT models (multilingual and German).
-On both historical test sets, in-domain LMs yield the best results (outperforming those of Riedl et
-al.), all the more so when the temporal overlap between embedding and task-specific training data
-is large. This demonstrates that the selection of the language model corpus plays an important role,
-and that unlabelled data close in time might have more impact than more (and difficult to obtain)
-labelled data.
-41Corpus presented in Section 5.2.2Named Entity Recognition and Classification on Historical Documents: A Survey 33
-With the objective of developing a versatile approach that performs decently on texts of different
-epochs without intense adaptation, Labusch et al. [ 108] experiment with BERT under different
-pre-training and fine-tuning settings. In a nutshell, they apply a model based on multilingual
-BERT embeddings, which is further pre-trained on large OCRed historical German unlabelled
-data (the Digital Collection of the Berlin State Library) and subsequently fine-tuned on several
-NE-labelled datasets (CoNLL-03, GermEval, and the German part of Europeana NER corpora).
-Tested across different contemporary/historical dataset pairs (similar to the all-corpora crossing of
-Riedl et al. [ 158]), it appears that additional in-domain pre-training is most of the time beneficial
-for historical pairs, while performances worsen on contemporary ones. The combination of several
-task-specific training datasets has positive yet less important impact than BERT pre-training, as
-already observed by Schweter et al. [ 172]. Overall, this work shows that an appropriately pre-trained
-BERT model delivers decent recognition performances in a variety of settings. In order to further
-improve them, authors purpose to use the BERT large instead of the BERT base model, to build
-more historical labelled training data, and to improve the OCR quality of the collections.
-The same spirit of combinatorial optimization drove the work of Todorov et al. [ 191] and
-Schweter et al. [ 173] in the context of HIPE-2020. Todorov et al. build on the bidirectional LSTM-
-CRF architecture of Lample et al. and introduce a multi-task approach by splitting the top layers
-for each entity type. Their general embedding layer combines a multitude of embeddings, on
-the level of characters, sub-words and words; some newly trained by the authors, as well as pre-
-trained BERT and HIPE’s in-domain fastText embeddings. They also vary the segmentation of
-the input: line segmentation, document segmentation as well as sub-document segmentation for
-long documents. No additional NER training material was used for German and French, while for
-English, the Groningen Meaning Bank42was adapted for training. Results suggest that splitting
-the top layers for each entity type is not beneficial. However, the addition of various embeddings
-improves the performance, as shown in the very detailed ablation test report. In this regard,
-character-level and BERT embeddings are particularly important, while in-domain embeddings
-contribute mainly to recall. Fine-tuning pre-trained embeddings did not prove beneficial. Using
-(sub-)document segmentation clearly improved results when compared to the line segmentation
-found in newspapers, emphasising once again the importance of context. Post-campaign F-scores
-for coarse literal NER are 75%and 66%for French and German (strict setting). English experiments
-yielded poor results, certainly due to the time and linguistic gaps between training and test data,
-and the pretty bad OCR quality of the material (in the same way as for Provatorova et al . [152] and
-Kristanti et al. [105]).
-For their part, Schweter et al. [ 173] focused on German and experimented with ensembling
-different word and subword embeddings (modern fastText and historical self-trained and HIPE
-flair embeddings), as well as transformer-based language models (trained on modern and historical
-data), all integrated by the neural Flair NER tagging framework [ 3]. They used a state-of-the-
-art BiLSTM with an on-top CRF layer as proposed by [ 89], and perform sentence splitting and
-hyphen normalisation as pre-processing. To identify the optimal combination of embeddings
-and LMs, authors first selected the best embeddings for each type before combining them. Using
-richer representations (fastText<flair<BERT) leads to better results each time. Among the options,
-Wikipedia fastText embeddings proved better than the Common Crawl ones, suggesting that similar
-data (news) is more beneficial than larger data for static representations; HIPE flair embeddings
-proved better than other historical ones, likely because of their larger training data size and data
-proximity; and BERT LM trained on large data proved better than the one trained on historical
-42https://gmb.let.rug.nl/34 Ehrmann et al.
-(smaller) data. The best final combination includes fastText and BERT, leading to 65%F-score on
-coarse literal NER (strict setting).
-Finally, Boros et al . [25] also tackled NER tagging for HIPE-2020 in all languages and achieved
-best results. They used a hierarchical transformer-based model [ 196] built upon BERT in a multi-
-task learning setting. On top of the pre-trained BERT blocks (multilingual BERT for all languages,
-additionally Europeana BERT for German43and CamemBERT for French [ 124]), two task-specific
-transformer layers were optionally added to alleviate data sparsity issues, for instance out-of-
-vocabulary words, spelling variations, or OCR errors in the HIPE dataset. A state-of-the-art CRF
-layer was added on top in order to model the context dependencies between entity tags. For base
-BERT with a limited context of 512 sub-tokens, documents are too long and newspaper lines are
-too short for proper contextualization. Therefore, an important pre-processing step consisted in the
-reconstruction of hyphenated words and in sentence segmentation. For the two languages with in-
-domain training data (French and German), their best run consisted in BERT fine-tuning, completed
-with the two stacked transformer blocks and the CRF layer. For English without in-domain training
-data, two options for fine-tuning were tested: a) training on monolingual CoNLL-03 data, and b)
-transfer learning by training on the French and German HIPE data. Both options worked better
-without transformer layers, and training on the French and German HIPE data led to better results.
-Final F-scores for coarse literal NER were 84%,79% and 63% for French, German and English
-respectively (strict setting).
-Conclusion on deep learning approaches. What conclusions can be drawn from all this? First,
-the twenty or so papers reviewed above illustrate the growing interest of researchers and practi-
-tioners from different fields in the application of deep learning approaches to NER on historical
-collections. Second, it is obvious that these many publications also equate with a great diversity
-in terms of document, system and task settings. Apart from the historical German [ 108,158,172]
-and HIPE papers, most publications use different datasets and evaluation settings, which pre-
-vents result comparison; what is more, the sensitivity of DL approaches to experimental settings
-(pre-processing, embeddings, hyper-parameters, hardware) usually undermines any attempt to
-compare or reproduce experiments, and often leads to misconceptions about what works and what
-does not [ 202]. As shown in the DL literature review above, what is reported can sometimes be
-contradictory. However, and with this in mind, a few conclusions can be drawn:
-–State-of-the-art BiLSTM architectures achieve very good performances and largely outper-
-form traditional CRFs, except in small data contexts and on very regular entities. As inference
-layer, CRF is a better choice than softmax (also confirmed by Yang et al . [202] ). Yet, in the
-fast-changing DL landscape, transformer-based networks are already taking over BiLSTM.
-–Character and sub-word information is beneficial and helps the model to deal with OOV
-words, presumably historical spelling variations and OCR errors. CNN appears to be a better
-option than LSTM to learn character embeddings.
-–As for word representation, the richer the better. The same neural architecture performs
-better with character or word-based contextualised embeddings than with static ones, and
-even better with stacked embeddings. The combination of flair or fastText embeddings plus
-a BERT language model seems to provide an appropriate mix of morphological and lexical
-information. Contextualised representations also have positive impact in low resource setting.
-–Pre-trained modern embeddings prove to transfer reasonably well to historical texts, even
-more when learned on very large textual data. As expected, in-domain embeddings contribute
-positively to performances most of the time, and the temporal proximity between the corpora
-43https://github.com/stefan-it/europeana-bertNamed Entity Recognition and Classification on Historical Documents: A Survey 35
-from which embeddings are derived and the targeted historical material seems to play an
-important role. Although a combination of generic and historical prior knowledge is likely to
-increase performances, what is best between very large modern vs. in-domain LMs remains
-an open question.
-–Careful pre-processing of input text (word de-hyphenation, sentence segmentation) in order
-to work with valid linguistic units appears to be a key factor.
-Ultimately, apart from clearly outperforming traditional ML and rule-based systems, the most
-compelling aspect of DL approaches is certainly their transferability; if much still need to be
-investigated, the possibility of having systems performing (relatively) well across historical settings—
-or a subset thereof—seems to be an achievable goal.
-7 STRATEGIES TO DEAL WITH SPECIFIC CHALLENGES
-We report here on the main strategies implemented by the different types of approaches to overcome
-OCR noise, adapt to language shifts and deal with lack of resources. Table 7 provides a synthetic
-view of the challenges, their impact, and the possible solutions to address them.
-7.1 Dealing with noisy input
-There exist two main strategies to deal with OCR and OLR noise: adapting the input, i.e. correcting
-the text before parsing it, or adapting the tool, i.e. making the NER system capable of dealing with
-the noise. Let us recall here that OCR and OLR noise mostly correspond to: misrecognised characters,
-erroneously truncated or connected word parts, spurious word hyphenations and incorrect mix of
-textual segments, all of these translating into OOV words and/or inconsistencies affecting both
-entities and their context.
-The first strategy corresponds to OCR/OLR post-correction and aims at recovering correct word
-forms and rebuilding linguistically motivated token sequences. Such processes depend on the
-specifics of each input (e.g. in terms of layout, typographic conventions, presence of marginalia)
-and are not easy to implement given the countless erroneous punctuation marks added by OCR
-and the subtle difference between soft and hard hyphens, an information often lost through the
-different digital versions of a document. In this context, most work apply a mix of well-known
-and ad hoc correction strategies based on corpus examination, including: a) correction of the ‘long
-s’, an archaic from of the ‘s’ letter systematically confused with ‘f’ by OCR engines [ 6]; b) word
-de-hyphenation, which consists in removing the end-of-line soft hyphens and checking the validity
-of the resulting word form [ 6,44]; c) word OCR post-correction based on the edit distance between
-input tokens and a list of most frequent OOV words manually corrected (allowing to correct
-the majority of mistakes) [ 44]; d) application of a generic spelling correction system [ 93]; and e)
-application of sentence segmentation [ 25,137]. Word de-hyphenation and sentence segmentation
-(or an approximation of it, depending on the quality of the input) are beneficial for all types of
-systems but are particularly critical for neural-based systems. Word correction have positive albeit
-moderate and irregular impact on the performances [ 44], which illustrates the difficulty of OCR
-post-correction and raises the question of under which conditions it is most beneficial. Using a
-generic spelling correction system, Huynh et al. [ 93] precisely leave aside the fine-tuning of OCR
-correction to focus, instead, on the question of when to apply it. They show that post-OCR correction
-is most beneficial with character and word-level error rates above 3% and 20% respectively, while
-it degrades NER performances for lower rates, due to spurious corrections. Overall, input noise
-correction or reduction is beneficial but should be adjusted according to the type and importance
-of noise.36 Ehrmann et al.
-Challenges Impact Possible solutions
-Noisy input
-sparser feature space,
-low recall.OCR post-correction
-string similarity
-historical LMs
-in-domain training data
-sub-word tokenisation•OOV
-•broken token sequences
-Dynamics of language
-sparser feature space,
-low recall.normalisation
-historical LMs
-in-domain training data•spelling variations
-•name irregularities
-•entity drift
-Lack of resources
-limited learning capacities,
-limited system comparison.transfer learning
-active learning
-data augmentation
-resource sharing•inappropriate typologies
-•lack of NE-annotated corpora
-•paucity of historical LMs
-Table 7. NER on historical documents: main challenges, their impact, and possible solutions.
-Another approach is to leave the input untouched but to provide the system with relevant
-information about the noise, mainly in the form of embedded language representations for neural-
-based approaches. In this regard, word embeddings computed at the character and sub-words
-levels (fastText [ 21]) as well as character-level LM embeddings (flair [ 4]) are particularly efficient
-in dealing with miss-recognised characters. For example, a BiLSTM-CRF model based on flair
-historical embeddings could correctly recognise and label T«i*louse (Toulouse) and Caa.Qrs (Ca-
-hors) as Location , and o˚an (Jean) asPerson [20]. Beside input representations, transformer-based
-architectures such as BERT integrate sub-word tokenizers based on e.g. Byte-Pair Encoding [ 174]
-or WordPiece [ 199] algorithms. Such tokenisation allows to consider word pieces not present in
-the LM vocabulary, thereby learning representations of OOV words. This is however not the total
-answer since resulting sub-words depend on the anatomy of the misspelling: if character insertion
-mostly results in known sub-word units, substitution or deletion produce uncommon units which
-still cause difficulties [ 183]. Boros et al. [ 24] (outperforming their previous work [ 25]) carried out an
-in-depth error analysis of the performances of different variants of a BERT-based system augmented
-(or not) with extra transformer layers, considering among others the percentage of OOV words and
-the severeness of OCR noise in entity mentions. Conclusions are that the representation capacity
-of the extra layers is beneficial for both recall and precision: while misspelled entities are better
-recognised in general, the system with extra layers does not over-predict entities, as is the case for
-the non-augmented system. Authors also highlight the possible over-fitting of the base model on
-OCR-related patterns in frequent entities, a question on which further research is necessary.
-In general, language models and transformers thus appear as good options to deal with noisy
-input with the least effort, but the effectiveness of targeted correction heuristics should not be
-underrated. Let us conclude with Fig. 2, which illustrates the results of three different systems on
-noisy texts.
-7.2 Dealing with dynamics of language
-Similarly to noise, adaptation to dynamics of language aims at reducing the gap between the text to
-analyse and the knowledge of the system. Let us recall the issues at stake here: historical spelling
-variations, evolving naming conventions, and entity drift. There is no single and clear-cut answer
-to each of these issues, but rather a set of possibilities which can help address them.Named Entity Recognition and Classification on Historical Documents: A Survey 37
-NE GT -CRF -BiLSTM -BERT -PERSON
-English: [who] was the general Chapedelaine, who was never mentioned in [the military annals]PERSONPERSONétaitle généraiChapecUlaine, dontiln'ajamaisétéquestion dansétaitle généraiChapecUlaine, dontiln'ajamaisétéquestion dansétaitle généraiChapecUlaine, dontiln'ajamaisétéquestion dansétaitle généraiChapecUlaine, dontiln'ajamaisétéquestion dans(1)
-NE GT -CRF -BiLSTM -BERT -PRODUCT
-PRODUCTEnglish: "[the] attacks that the Wäschfra [has taken the liberty to conduct] against [...]". Angriffe, welches sichDie „3Sd)cb.fra" gegenAngriffe, welches sichDie „3Sd)cb.fra" gegenAngriffe, welches sichDie „3Sd)cb.fra" gegenAngriffe, welches sichDie „3Sd)cb.fra" gegen(2)
-Fig. 2. Results of a CRF, BiLSTM-CRF and BERT-based NER systems on excerpts from the French Swiss
-Gazette de Lausanne of August 4 1818, p.4 (1), and from the German Luxembourgian Luxemburger Wort of
-July 21 1868, p.2 (2) (HIPE data), compared to the ground truth (NE GT).
-As previously mentioned, rule-based and traditional ML systems using gazetteers often suffer
-from low recall due to vocabulary mismatches between name variants and gazetteer entries. Several
-approaches can help alleviate this issue. First, the lookup conditions can be loosened using a string
-similarity measure with an empirically determined threshold. In this respect, Borin et al. [ 23]
-showed that allowing a small Levenshtein distance between input tokens and gazetteers entries
-allows to capture orthographic variations between 19C and 20C Swedish names and thus to increase
-the performances of their rule-based system, particularly recall (+5 percentage points). Another
-option consists in normalising historical spellings using transformation rules, either manually
-crafted, such as in Neudecker [ 132] and Platas et al. [ 150], or automatically acquired on aligned
-texts such as in Kogkitsidou et al. [ 104]. Results obtained on normalised versions of texts or entities
-are usually better, though a somehow contrasted picture emerges depending on the system, the
-type of texts, and the time period — as per OCR post-correction.
-When trying to adapt the knowledge of the system rather than the input, the key factor is,
-not surprisingly, temporal proximity. Here word embeddings and language models derived from
-temporally close corpora seem to be better able to capture historical language idiosyncrasies,
-including spelling variations [ 108,172]. There is no clear evidence yet regarding what is best
-between pre-training from scratch on historical texts or fine-tuning a modern model on historical
-texts (in a supervised or unsupervised manner) and further research is necessary on this point.
-Beyond technicalities, an important aspect to consider when adapting a system to time is its
-application scope, i.e. whether it is intended to perform well on a unique target (one document
-type, one time period) or across several.
-7.3 Dealing with the lack of resources
-As emphasised in Section 4.4, the situation of lack of resources is not unique to historical NER and
-corresponds here to inappropriate typologies, and lack of labelled and unlabelled historical corpora.
-With respect to typologies, one can only adapt and/or define a typology when existing tag sets
-are not appropriate [ 2,150,189]. As easy as it may seem, two comments are in order here: first,
-this represents a time-consuming process which requires several expertise (in linguistics, in the
-historical domain at hands, and in knowledge representation) and needs to be documented, notably
-via annotation guidelines. Specifically, phenomena such as nested entities and metonymy did not38 Ehrmann et al.
-received much attention in modern NER but are of high interest for humanists’ (re)search needs.
-Second, careful attention should be paid to typologies interoperability, without which resources
-are mere silos and need an extra mapping step [161].
-Several strategies can be adopted to cope with the lack of training data. The most widely used so
-far is transfer learning, as described in Section 3.4.3. Another option is active learning, where a ML
-system asks an oracle (or a user) to select the most relevant examples to consider, thereby lowering
-the number of data points required to learn a model. This is the approach adopted by Erdmann et
-al. [60] to recognise entities in various Latin classical texts, based on an active learning pipeline
-able to predict how many and which sentences need to be annotated to achieve a certain degree
-of accuracy, and later on released as toolkit to build custom NER models for the humanities [ 61].
-Finally, another strategy is data augmentation, where an existing data set is expanded via the
-transformation of training instances without changing their label. This approach, which has not yet
-been deployed in a historical context, has been successfully tested by Dai et al. [ 39] on biomedical
-data, where several data augmentation techniques, in isolation or in combination, led to improved
-performance, especially with small training datasets.
-8 CONCLUSIONS AND OUTLOOK
-We presented an extensive survey of research, published in the last 20 years, on the topic of NER
-on historical documents. We introduced the main challenges of historical NER, namely document
-type and domain variety, noisy input, dynamics of language, and lack of resources. We inventoried
-existing resources available for historical NER (typologies and annotation guidelines, annotated
-corpora and language representations), and surveyed the approaches developed to date to tackle
-this task, paying special attention to how they adapt to historical settings.
-What clearly emerges from this review is, first, that research on historical NER has gained real
-momentum over the last decade. The availability of machine-readable historical texts coupled with
-the recent advances in deep learning has led to increased attention from researchers and cultural
-heritage practitioners for what has now become a cornerstone of semantic indexing of historical
-documents. Second, the body of research on historical NER started by following state-of-the-art
-approaches in modern NER (with rule-based and then traditional ML approaches), before fully
-experimenting with the various possibilities arising from diachronic transfer learning with neural-
-based approaches. This last development helped increase the performances of NER systems on
-historical material with F-scores going from 60-70% on average for rule-based and traditional ML
-systems to, for the best neural systems, 80%. As of today, it is therefore possible to design systems
-capable of dealing with historical and noisy inputs, whose performances almost compete with
-those obtained on contemporary texts. This success, however, should not conceal the progress still
-to be made. In this regard, we outline a set of key priorities for the next generation of historical
-NER systems:
-(1)Transferability . We emphasise that beyond addressing a specific type of document and/or time
-period lies the question of systems’ portability across historical settings. While addressing
-system adaptability across both time and domain at once might be overly ambitious for
-the time being, having systems performing equally well across one or the other is highly
-desirable—especially for cultural heritage institutions—and represents to next great challenge.
-In this respect, we encourage to pursue and especially systematise further the transfer learning
-experiments undertaken so far.
-(2)Robustness . Although a great deal of headway has been made to enable systems to deal
-with atypical historical inputs, we highlight that OCR/OLR noise and historical spellings are
-still the main sources of errors of NER systems on historical texts. One of the way forwardNamed Entity Recognition and Classification on Historical Documents: A Survey 39
-includes a better assessment of which type of noise is detrimental and to which extent in
-order to devise more systematic and focused strategies to deal with it.
-(3)System comparability . The systematic comparison of the advantages and shortcomings of
-approaches to historical NER was made difficult because of the variety of settings to which
-they applied (domains, time periods, languages) and of the corpora against which they were
-evaluated. We stress the importance of gold standards and of shared tasks on historical
-material to enable system comparison and drive progress in historical NER.
-(4)Finer-grained historical NER . The (re)search interests of scholars go beyond the recognition
-of main entity types and we underline the need to carry out finer-grained NER, taking into
-account e.g. nested entities, entity name composition, and entity metonymy.
-(5)Resource sharing . All recent advances in deep learning were made possible by the availability
-of large-scale textual data. While the sharing of such resources has just begun, we emphasise
-the need for access to large-scale historical textual data or to language models derived thereof.
-This sharing should also extend to typologies, annotation guidelines, and training material,
-with a special attention to interoperability.
-NER on historical documents is an exciting field of research with high added-value for both NLP
-researchers and digital scholars. While the first can test the robustness of their approaches and gain
-new insights with respect to domain, language and time adaptation, the second can benefit from
-more accurate semantic indexing and text understanding of historical material. Lastly, we wish to
-mention two facets which, even if not directly related to development of historical NER systems,
-should be considered while working on this topic: any large-scale endeavour around historical NEs
-should acknowledge ethical and legal obligations related to personal data protection, and is most
-likely to be useful if humanities scholarship knowledge and needs are taken into account within an
-interdisciplinary framework.
-ACKNOWLEDGMENTS
-The work of Maud Ehrmann and Matteo Romanello was supported by the Swiss National Science
-Foundation under the grants number CR-SII5_173719 ( Impresso - Media Monitoring of the Past)
-and number PZ00P1_186033 (only for MR). The work of Ahmed Hamdi, Elvys Linhares Pontes
-(now employed at the Trading Central Labs company) and Antoine Doucet was supported by the
-European Union’s Horizon 2020 research and innovation program under grant 770299 (NewsEye).
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